(upbeat music) >> We are back, approaching the finish line here at Supercomputing 22, our last interview of the day, our last interview of the show. And I have to say Dave Nicholson, my co-host, My name is Paul Gillin. I've been attending trade shows for 40 years Dave, I've never been to one like this. The type of people who are here, the type of problems they're solving, what they talk about, the trade shows are typically, they're so speeds and feeds. They're so financial, they're so ROI, they all sound the same after a while. This is truly a different event. Do you get that sense? >> A hundred percent. Now, I've been attending trade shows for 10 years since I was 19, in other words, so I don't have necessarily your depth. No, but seriously, Paul, totally, completely, completely different than any other conference. First of all, there's the absolute allure of looking at the latest and greatest, coolest stuff. I mean, when you have NASA lecturing on things when you have Lawrence Livermore Labs that we're going to be talking to here in a second it's a completely different story. You have all of the academics you have students who are in competition and also interviewing with organizations. It's phenomenal. I've had chills a lot this week. >> And I guess our last two guests sort of represent that cross section. Armando Acosta, director of HPC Solutions, High Performance Solutions at Dell. And Matt Leininger, who is the HPC Strategist at Lawrence Livermore National Laboratory. Now, there is perhaps, I don't know you can correct me on this, but perhaps no institution in the world that uses more computing cycles than Lawrence Livermore National Laboratory and is always on the leading edge of what's going on in Supercomputing. And so we want to talk to both of you about that. Thank you. Thank you for joining us today. >> Sure, glad to be here. >> For having us. >> Let's start with you, Armando. Well, let's talk about the juxtaposition of the two of you. I would not have thought of LLNL as being a Dell reference account in the past. Tell us about the background of your relationship and what you're providing to the laboratory. >> Yeah, so we're really excited to be working with Lawrence Livermore, working with Matt. But actually this process started about two years ago. So we started looking at essentially what was coming down the pipeline. You know, what were the customer requirements. What did we need in order to make Matt successful. And so the beauty of this project is that we've been talking about this for two years, and now it's finally coming to fruition. And now we're actually delivering systems and delivering racks of systems. But what I really appreciate is Matt coming to us, us working together for two years and really trying to understand what are the requirements, what's the schedule, what do we need to hit in order to make them successful >> At Lawrence Livermore, what drives your computing requirements I guess? You're working on some very, very big problems but a lot of very complex problems. How do you decide what you need to procure to address them? >> Well, that's a difficult challenge. I mean, our mission is a national security mission dealing with making sure that we do our part to provide the high performance computing capabilities to the US Department of Energy's National Nuclear Security Administration. We do that through the Advanced Simulation computing program. Its goal is to provide that computing power to make sure that the US nuclear rep of the stockpile is safe, secure, and effective. So how we go about doing that? There's a lot of work involved. We have multiple platform lines that we accomplish that goal with. One of them is the advanced technology systems. Those are the ones you've heard about a lot, they're pushing towards exit scale, the GPU technologies incorporated into those. We also have a second line, a platform line, called the Commodity Technology Systems. That's where right now we're partnering with Dell on the latest generation of those. Those systems are a little more conservative, they're right now CPU only driven but they're also intended to be the everyday work horses. So those are the first systems our users get on. It's very easy for them to get their applications up and running. They're the first things they use usually on a day to day basis. They run a lot of small to medium size jobs that you need to do to figure out how to most effectively use what workloads you need to move to the even larger systems to accomplish our mission goals. >> The workhorses. >> Yeah. >> What have you seen here these last few days of the show, what excites you? What are the most interesting things you've seen? >> There's all kinds of things that are interesting. Probably most interesting ones I can't talk about in public, unfortunately, 'cause of NDA agreements, of course. But it's always exciting to be here at Supercomputing. It's always exciting to see the products that we've been working with industry and co-designing with them on for, you know, several years before the public actually sees them. That's always an exciting part of the conference as well specifically with CTS-2, it's exciting. As was mentioned before, I've been working with Dell for nearly two years on this, but the systems first started being delivered this past August. And so we're just taking the initial deliveries of those. We've deployed, you know, roughly about 1600 nodes now but that'll ramp up to over 6,000 nodes over the next three or four months. >> So how does this work intersect with Sandia and Los Alamos? Explain to us the relationship there. >> Right, so those three laboratories are the laboratories under the National Nuclear Security Administration. We partner together on CTS. So the architectures, as you were asking, how do we define these things, it's the labs coming together. Those three laboratories we define what we need for that architecture. We have a joint procurement that is run out of Livermore but then the systems are deployed at all three laboratories. And then they serve the programs that I mentioned for each laboratory as well. >> I've worked in this space for a very long time you know I've worked with agencies where the closest I got to anything they were actually doing was the sort of guest suite outside the secure area. And sometimes there are challenges when you're communicating, it's like you have a partner like Dell who has all of these things to offer, all of these ideas. You have requirements, but maybe you can't share 100% of what you need to do. How do you navigate that? Who makes the decision about what can be revealed in these conversations? You talk about NDA in terms of what's been shared with you, you may be limited in terms of what you can share with vendors. Does that cause inefficiency? >> To some degree. I mean, we do a good job within the NSA of understanding what our applications need and then mapping that to technical requirements that we can talk about with vendors. We also have kind of in between that we've done this for many years. A recent example is of course with the exit scale computing program and some things it's doing creating proxy apps or mini apps that are smaller versions of some of the things that we are important to us. Some application areas are important to us, hydrodynamics, material science, things like that. And so we can collaborate with vendors on those proxy apps to co-design systems and tweak the architectures. In fact, we've done a little bit that with CTS-2, not as much in CTS as maybe in the ATS platforms but that kind of general idea of how we collaborate through these proxy applications is something we've used across platforms. >> Now is Dell one of your co-design partners? >> In CTS-2 absolutely, yep. >> And how, what aspects of CTS-2 are you working on with Dell? >> Well, the architecture itself was the first, you know thing we worked with them on, we had a procurement come out, you know they bid an architecture on that. We had worked with them, you know but previously on our requirements, understanding what our requirements are. But that architecture today is based on the fourth generation Intel Xeon that you've heard a lot about at the conference. We are one of the first customers to get those systems in. All the systems are interconnected together with the Cornell Network's Omni-Path Network that we've used before and are very excited about as well. And we build up from there. The systems get integrated in by the operations teams at the laboratory. They get integrated into our production computing environment. Dell is really responsible, you know for designing these systems and delivering to the laboratories. The laboratories then work with Dell. We have a software stack that we provide on top of that called TOSS, for Tri-Lab Operating System. It's based on Redhead Enterprise Linux. But the goal there is that it allows us, a common user environment, a common simulation environment across not only CTS-2, but maybe older systems we have and even the larger systems that we'll be deploying as well. So from a user perspective they see a common user interface, a common environment across all the different platforms that they use at Livermore and the other laboratories. >> And Armando, what does Dell get out of the co-design arrangement with the lab? >> Well, we get to make sure that they're successful. But the other big thing that we want to do, is typically when you think about Dell and HPC, a lot of people don't make that connection together. And so what we're trying to do is make sure that, you know they know that, hey, whether you're a work group customer at the smallest end or a super computer customer at the highest end, Dell wants to make sure that we have the right setup portfolio to match any needs across this. But what we were really excited about this, this is kind of our, you know big CTS-2 first thing we've done together. And so, you know, hopefully this has been successful. We've made Matt happy and we look forward to the future what we can do with bigger and bigger things. >> So will the labs be okay with Dell coming up with a marketing campaign that said something like, "We can't confirm that alien technology is being reverse engineered." >> Yeah, that would fly. >> I mean that would be right, right? And I have to ask you the question directly and the way you can answer it is by smiling like you're thinking, what a stupid question. Are you reverse engineering alien technology at the labs? >> Yeah, you'd have to suck the PR office. >> Okay, okay. (all laughing) >> Good answer. >> No, but it is fascinating because to a degree it's like you could say, yeah, we're working together but if you really want to dig into it, it's like, "Well I kind of can't tell you exactly how some of this stuff is." Do you consider anything that you do from a technology perspective, not what you're doing with it, but the actual stack, do you try to design proprietary things into the stack or do you say, "No, no, no, we're going to go with standards and then what we do with it is proprietary and secret."? >> Yeah, it's more the latter. >> Is the latter? Yeah, yeah, yeah. So you're not going to try to reverse engineer the industry? >> No, no. We want the solutions that we develop to enhance the industry to be able to apply to a broader market so that we can, you know, gain from the volume of that market, the lower cost that they would enable, right? If we go off and develop more and more customized solutions that can be extraordinarily expensive. And so we we're really looking to leverage the wider market, but do what we can to influence that, to develop key technologies that we and others need that can enable us in the high forms computing space. >> We were talking with Satish Iyer from Dell earlier about validated designs, Dell's reference designs for for pharma and for manufacturing, in HPC are you seeing that HPC, Armando, and is coming together traditionally and more of an academic research discipline beginning to come together with commercial applications? And are these two markets beginning to blend? >> Yeah, I mean so here's what's happening, is you have this convergence of HPC, AI and data analytics. And so when you have that combination of those three workloads they're applicable across many vertical markets, right? Whether it's financial services, whether it's life science, government and research. But what's interesting, and Matt won't brag about, but a lot of stuff that happens in the DoE labs trickles down to the enterprise space, trickles down to the commercial space because these guys know how to do it at scale, they know how to do it efficiently and they know how to hit the mark. And so a lot of customers say, "Hey we want what CTS-2 does," right? And so it's very interesting. The way I love it is their process the way they do the RFP process. Matt talked about the benchmarks and helping us understand, hey here's kind of the mark you have to hit. And then at the same time, you know if we make them successful then obviously it's better for all of us, right? You know, I want to secure nuclear stock pile so I hope everybody else does as well. >> The software stack you mentioned, I think Tia? >> TOSS. >> TOSS. >> Yeah. >> How did that come about? Why did you feel the need to develop your own software stack? >> It originated back, you know, even 20 years ago when we first started building Linux clusters when that was a crazy idea. Livermore and other laboratories were really the first to start doing that and then push them to larger and larger scales. And it was key to have Linux running on that at the time. And so we had the. >> So 20 years ago you knew you wanted to run on Linux? >> Was 20 years ago, yeah, yeah. And we started doing that but we needed a way to have a version of Linux that we could partner with someone on that would do, you know, the support, you know, just like you get from an EoS vendor, right? Security support and other things. But then layer on top of that, all the HPC stuff you need either to run the system, to set up the system, to support our user base. And that evolved into to TOSS which is the Tri-Lab Operating System. Now it's based on the latest version of Redhead Enterprise Linux, as I mentioned before, with all the other HPC magic, so to speak and all that HPC magic is open source things. It's not stuff, it may be things that we develop but it's nothing closed source. So all that's there we run it across all these different environments as I mentioned before. And it really originated back in the early days of, you know, Beowulf clusters, Linux clusters, as just needing something that we can use to run on multiple systems and start creating that common environment at Livermore and then eventually the other laboratories. >> How is a company like Dell, able to benefit from the open source work that's coming out of the labs? >> Well, when you look at the open source, I mean open source is good for everybody, right? Because if you make a open source tool available then people start essentially using that tool. And so if we can make that open source tool more robust and get more people using it, it gets more enterprise ready. And so with that, you know, we're all about open source we're all about standards and really about raising all boats 'cause that's what open source is all about. >> And with that, we are out of time. This is our 28th interview of SC22 and you're taking us out on a high note. Armando Acosta, director of HPC Solutions at Dell. Matt Leininger, HPC Strategist, Lawrence Livermore National Laboratories. Great discussion. Hopefully it was a good show for you. Fascinating show for us and thanks for being with us today. >> Thank you very much. >> Thank you for having us >> Dave it's been a pleasure. >> Absolutely. >> Hope we'll be back next year. >> Can't believe, went by fast. Absolutely at SC23. >> We hope you'll be back next year. This is Paul Gillin. That's a wrap, with Dave Nicholson for theCUBE. See here in next time. (soft upbear music)

>>from around the globe. It's the queue with digital coverage of AWS Public sector online brought to you by Amazon Web services. Welcome back. I'm stew minimum. And this is the Cube coverage of Amazon Web service Public sectors, online summit Always love. We have phenomenal practitioner discussion. Of course, public sector includes both government agencies, universities, education, broad swath, you know, inside that ecosystem and some really, you know, important and timely discussion we're having. Of course, with the global pandemic Kobe 19 happening. I'm really happy to welcome to the program Wei Li, who is a PhD and principal investigator as well as an assistant professor both Children National Research Institute associated with George Washington University way Thank you so much for joining us. >>Yeah. Thank you for the opportunity. We're here. >>Alright. Why don't we start with Ah, give us a little bit of you know, your research focus in general. And you know what projects it is that you're working on these days? Yeah, >>sure. So, yeah, so hello, everyone. So our laboratory is many interested in using computational biology and jim editing approaches to understand human genome and human disease. And we're particularly interesting in one gene editing technology will be called CRISPR screening. So this is a fascinating, high for proven technology because it tells you whether one doctor 20,000 human genes are connected with some certain pieces fit in type in one single experiment. So in the possibly developed some of the widely used every reasons to analyze the swimming data has been downloaded off by over 60,000 times. So it's really popular, and right now there are a couple of going projects. But basically we are trying to, for example, problem in machine learning and data mining approaches to find new clues of human disease from the original mix and screening big data on. We also collaborated with a lot of blacks around the world and to use this technology to use this technology to find new cures and drugs for cancer and other decisions. So this is the basic all the way off our current research programmes. Interns off the Conradi 19 research. I think one of the major projects we are having is that, um, we noticed that Christmas winning and other similar screening methods has been widely used in many years. Many research adapted to study waters infection. So in the past 10 years we have seen people you are using their Christmas screening and our AI suite, for example, to study HIV is a car wires, best bars, Western ire virus, Ebola influencers and also coronavirus. So that raises an interesting question from us if we collect all the screening data together. But these viruses, what a new information can we find that we cannot identify for the single study, for example, coe and identify new patterns or new human genes that are that are common in responsible for many different viruses? Type of all, we can find some genes that I work only for some certain people viruses so more well, we know that there are a lot of drugs that target different genes, and we are particularly interested in, for example, can repurpose some of these drugs to treat different hyper viruses, including Kobe, 18 19. So that's the one of the major profits off ongoing research, right and left ready to call the idea, writing So India. And we hope that we can find some new new Jim functions that after that that are broader, really essential for different hyper viruses. I also new drug targets that can potentially treat existing a new drug existing and new viruses, including compared to 19 >>Yeah, crisper. Shown a lot of promise is definitely a lot of excitement in the research community to be ableto work on this. You talked a little bit about, you know, big data, obviously a lot of computational power required to do some of the things you're talking about. Can you speak a little bit to the partnership between computer science and the medicine? How do you make sure on that? You know, there's that marrying of, you know, the people in the technology focus in the medical space. >>Yeah, so I think, Yeah, my my research background is actually from computer science. I call her on the grand graduates from their committed size. So I know a lot about some of the signs and have arisen. But right now it's quite interesting because our research for focus half on computer science and half on their medicine. So it's a complete heart experience, but it's really super was a super exciting to connect both women in science and medicine together. So I think most of the time we are focusing on the coding and the average analysis on. But at the same time, we also spent a lot of time like interpreting the results. In essence, we need a lot off. Yeah, knowledge from biology and medicine to make sense, to make our results since and interpret double in the end, we hope that our results can be They went into a son, for example, canonical, actionable solutions, including new drugs. >>Yeah, it's if you think about you know, the research space. You know, often you know its projects that you're taking months or years to investigate things for talking about the current code 19 pandemic. Of course, there's a critical need today for fast moving activities. So you know what? What are the outcomes from the cover? 19 aspects of of what you're working on. What are some of the outcomes that we might be able to help patients survivability and other things regarding, You know, this specific disease? >>Yeah, So I think there are two major are I would say there are two major benefits from their outcome of our research project. So the first the first thing is that we hope to find some genes that have that can be potentially drug targets. So if they are existing drug second heavily genes, then that would be perfect because we don't need to do anything. Apologies. We just need to try that. Extend existing drugs Toe cabinet is James and in the end, we hope that these drugs can have the broad on the wire. I would say the broad answer. Borrow activity. That means that and you leave, for example, if these drugs can be potentially used to treat Cooley 19 and sometimes in in several years later in the future if there's a new virus coming out. Hopefully they were doing like they're it's already the drugs that target known Gene. Hopefully, that's there were assume the noon numerous that never happened in something the future. But I hope that when the new risk is coming, we already have the new drugs to track it this way. Already have existing drugs to target these viruses, so that's one part and the alibis that way. We have, like, spend a lot of kind of, for example, collecting the genomics and screening data, and we are hoping that our research results can be freely accessible around the road by many different researchers in different laps. So that's why we are rely on AWS to build up there to process and to analyze the data as well as to, uh, to build up an integrated database and websites such that are the outcomes off our projects can be freely accessible around the world. Many other researchers. >>Yeah, great. I'm glad you connected the dots for us. For aws can you speak a little bit too? Obviously, Cloud has, you know, the ability for us to use, you know, nearly infinite computational capabilities. What's specific about AWS helps you along that project. Uh, let's start there. >>Yeah, So I think our AWS really helps us a lot because we developed on average and process their screening data actually takes, like, two or three days to Christmas one data. But if you were talking about, like, tens or hundreds or even thousands off the screen data existing, the high high performance cross team doesn't really help because it takes maybe years to finish. AWS provides, like flexible computing resources, especially the easy two instance that we can quickly deploy and process in military short amount of time. So our estimation is that we can reduce the amount of Time Media 2% to process the poverty Christmas. We need data from months to just a few days. So that's one part and the other guys that we are trying to build up the website and database, as I mentioned before, with which we host a large amount of data. And I think in that sense, AWS and the commuting instance as well as the AWS RDS service really helped us a lot because we don't need to worry too much about. There's a lot of the details of the after deployment off their database and the website. We just go ahead and use that as a service is really straightforward and save us a lot of kind of effort. >>Yeah, and you talk about the sharing of data. Information is so important, But of course it would, talking about medical data highly regulated. So you know what's important to the cloud to make sure that you can share with all the other researchers yet still make sure that there is the security and compliance that is required? >>Yeah, so yeah, that's a really good question. So right now, we don't really need to do if the patient information because all the data we get this from the public domain, it's It's both on the human sound lines, not on human patients. So we don't have their concerns about the privacy protections at this moment. But I think in the future, if you want to integrate genomics state our reach, this screen indeed A, which is already in my research plan. I think the highly secure AWS system actually really provided a really nice for us to do this job. >>Can you give us a little bit? Look forward as to where do you see this research going? What applicability is there before? What you're doing now? Both. You know, as this current pandemic plays out as well as applicability beyond Corp in 19. >>Yeah, sure, I think I think one of their major focus off our current, The company in 19 project is that we hope to find some drug targets tohave the broader under fire activity. So I think in the future, if they knew where it's coming out of the estimated locally in the 19 we hope that we are well prepared for that. I think in the future they're sharing as well as collateral cloud computing. You'll be becoming more and more important as you can see that most of us are working from home right now. So it's really critical to require us to have the platform toe accelerate accelerating sharing between research labs and around the world. And I think many different. I think aws provides this really nice preference for us to do this job well. >>Wei Li, thank you so much for sharing with our audience your updates, really important work. We wish your team the best of luck and hope that you also stay safe. >>Yeah, thank you so much. >>Alright, Stay with us for more coverage from AWS Public sector Summit online. I'm stew Minimum And thanks as always for watching the Cube >>Yeah, yeah, yeah, yeah, yeah

>> Narrator: From around the globe it's theCUBE with digital coverage of DockerCon Live 2020. Brought to you by Docker and its ecosystem partners. >> Hi, I'm Stu Miniman and welcome to theCUBE's coverage of DockerCon Live 2020. Really excited to be part of this online event. We've been involved with DockerCon for a long time, of course one of my favorite things is always to be able to talk to the practitioners. Of course we remember for years, Docker exploded onto the marketplace, millions of people downloaded it, using it. So joining me is Hui Xue, who is a Principal Deputy Director of Medical Signal Processing at the National Heart, Lung, and Blood Institute, which is part of the National Institute of Health. Hui, thank you so much for joining us. >> Thank you for inviting me. >> So let's start. Of course, the name of your institute, very specific. I think anyone in the United States knows the NIH. Tell us a little bit about your role there and kind of the scope of what your team covers. >> So I'm basically a researcher and developer of the medical imaging technology. We are the heart, lung and the blood, so we work and focus on imaging the heart. So what we exactly do is to develop the new and novel imaging technology and deploy them to the front of our clinical library, which Docker played an essential role in the process. So, yeah, that's what we do at NHLBI. >> Okay, excellent. So research, you know, of course in the medical field with the global pandemic gets a lot of attention. So you keyed it up there. Let's understand, where does containerization and Docker specifically play into the work that your team is doing? >> So, maybe I'd like to give an example which will suffice. So for example, we're working on the magnetic resonance imaging, MRI. Many of us may may already have been scanned. So we're using MRI to image the heart. What Docker plays, is Docker allow us to deploy our imaging technology to the clinical hospital. So we have a global deployment around 40 hospitals, a bit more, around the world. If we are for example develop a new AI-based image analysis for the heart image, what we do with Docker is we can put our model and software into the Docker so that our collaboration sites, they will pull the software that contains the latest technology, then use them for the patients, of course under the research agreement at NIH. Because Docker is so efficient, available globally, we can actually implement a continuous integration and testing, update the framework based on Docker. Then our collaborators would have the latest technology instead of, you know, in the traditional medical imaging in general, the iteration of technology is pretty slow. But with all this latest technology, and such like container Docker come into the field. It's actually relatively new. In the past two to three years, all these paradigm is, it's changing, certainly very exciting to us. It give us the flexibility we never had before to reach our customers, to reach other people in the world to help them. They also help us so that's a very good experience to have. >> Yeah that's pretty powerful what you're talking about there rather than you know, we install some equipment, who knows how often things get updated, how do you make sure to synchronize between different locations. Obviously the medical field highly regulated and being a government agency, talk a little bit about how you make sure you have the right version control, security is in place, how do all of those things sort out? >> Yes, that's an essential question. So firstly I want to clarify one thing. So it's not NIH who endorse Docker, it's us as researchers. We practiced Docker too and we trust its performance. This container technology is efficient, it's globally available and it's very secure. So all the communication between the container and the imaging equipment is encrypted. We also have all the paperwork it saved to set up to allow us to provide technology to our clinician. When they post the latest software, every version they put up into the Docker went through an automated integration test system. So every time they make a change, the newer version of software runs through a rigorous test, something like 200 gigabytes of data runs through and checked everything is still working. So the basic principle is we don't allow any version of the software to be delivered to customer without testing Docker. Let's say this container technology in general actually is 100% automating all this process, which actually give us a lot of freedom so we have a rather very small team here at NIH. Many people are actually very impressed by how many customer we support within this so small team. So the key reason is because we have a strongly utilized container technology, so its automation is unparalleled, certainly much better than anything I had before using this container technology. So that's actually the key to maintain the quality and the continuous service to our customers. >> Yeah, absolutely. Automation is something we've been talking about in the industry for a long time but if we implement it properly it can have a huge impact. Can you bring us inside a little bit, you know, what tools are you doing? How is that automation set up and managed? And how that fits into the Docker environment. >> So I kind of describe to be more specific. So we are using a continuous testing framework. There are several apps to be using a specific one to build on, which is an open source Python tool, rather small actually. What it can do is, this tool will set up at the service, then this service will watch for example our GitHub repo. Whenever I make a change or someone in the team makes a change for example, fix a bug, add a new feature, or maybe update a new AI model, we push the edge of the GitHub then there's a continuous building system that will notice, it will trigger the integration test run all inside Docker environment. So this is the key. What container technology offers is that we can have 100% reproducible runtime environment for our customers as the software provider, because in our particular use case we don't set up customer with the uniform hardware so they bought their own server around the world, so everyone may have slightly different hardware. We don't want that to get into our software experience. So Docker actually offers us the 100% control of the runtime environment which is very essential if we want to deliver a consistent medical imaging experience because most applications actually it's rather computational intensive, so they don't want something to run for like one minute in one site and maybe three minutes at another site. So what Docker place is that Docker will run all the integration tests. If everything pass then they pack the Docker image then send to the Docker Hub. Then all our collaborators around the world have new image then they will coordinate with them so they will find a proper time to update then they have the newer technology in time. So that's why Docker is such a useful tool for us. >> Yeah, absolutely. Okay, containerization in Docker really transformed the way a lot of those computational solutions happen. I'm wondering if you can explain a little bit more the stack that you're using if people that might not have looked at solutions for a couple of years think oh it's containers, it's dateless architectures, I'm not sure how it fits into my other network environment. Can you tell us what are you doing for the storage in the network? >> So we actually have a rather vertical integration in this medical imaging application, so we build our own service as the software, its backbone is C++ for the higher computational efficiency. There's lots of Python because these days AI model essential. What Docker provides, as I mentioned, uniform always this runtime environment so we have a fixed GCC version then if we want to go into that detail. Specific version of numerical library, certain versions of Python, will be using PyTorch a lot. So that's our AI backbone. Another way of using Docker is actually we deploy the same container into the Microsoft Azure cloud. That's another ability I found out about Docker, so we never need to change anything in our software development process, but the same container I give you must work everywhere on the cloud, on site, for our customers. This actually reduces the development cost, also improve our efficiency a lot. Another important aspect is this actually will improve customers', how do they say it, customer acceptance a lot because they go to one customer, tell them the software you are running is actually running on 30 other sites exactly the same up to the let's say heights there, so it's bit by bit consistent. This actually help us convince many people. Every time when I describe this process I think most people accept the idea. They actually appreciate the way how we deliver software to them because we always can falling back. So yes, here is another aspect. So we have many Docker images that's in the Docker Hub, so if one deployment fails, they can easily falling back. That's actually very important for medical imaging applications that fail because hospitals need to maintain their continuous level of service. So even we want to avoid this completely but yes occasionally, very occasionally, there will be some function not working or some new test case never covered before, then we give them an magnet then, falling back, that's actually also our policy and offered by the container technology. >> Yeah, absolutely. You brought up, many have said that the container is that atomic unit of building block and that portability around any platform environment. What about container orchestration? How are you managing these environments you talked about in the public cloud or in different environments? What are you doing for container orchestration? >> Actually our set-up might be the simplest case. So we basically have a private Docker repo which we paid, actually the Institute has paid. We have something like 50 or 100 private repos, then for every repo we have one specific Docker setup with different software versions of different, for example some image is for PyTorch another for TensorFlow depending on our application. Maybe some customer has the requirement to have rather small Docker image size then they have some trimmed down version of image. In this process, because it's still in a small number like 20, 30 active repo, we are actually managing it semi-automatically so we have the service running to push and pull, and loading back images but we actually configured this process here at the Institute whenever we feel we have something new to offer to the customer. Regarding managing this Docker image, it's actually another aspect for the medical image. So at the customer side, we had a lot of discussion with them for whether we want to set up a continuous automated app, but in the end they decided, they said they'd better have customers involved. Better have some people. So we were finally stopped there by, we noticed customer, there are something new to update then they will decide when to update, how to test. So this is another aspect. Even we have a very high level of confirmation using the container technology, we found it's not 100%. In some site, it's still better have human supervision to help because if the goal is to maintain 100% continuous service then in the end they need some experts on the field to test and verify. So that's how they are in the current stage of deployment of this Docker image. We found it's rather light-weight so even with a few people at NIH in our team, they can manage a rather large network globally, so it's really exciting for us. >> Excellent. Great. I guess final question, give us a little bit of a road map as to, you've already talked about leveraging AI in there, the various pieces, what are you looking for from Docker in the ecosystem, and your solution for the rest of the year? >> I would say the future definitely is on the cloud. One major direction we are trying to push is to go the clinical hospital, linking and use the cloud in building as a routine. So in current status, some of sites, hospital may be very conservative, they are afraid of the security, the connection, all kinds of issues related to cloud. But this scenario is changing rapidly, especially container technology contributes a lot on the cloud. So it makes the whole thing so easy, so reliable. So our next push is to move in lots of the application into the cloud only. So the model will be, for example, we have new AI applications. It may be only available on the cloud. If some customer is waiting to use them they will have to be willing to connect to the cloud and maybe sending data there and receive, for example, the AI apps from our running Docker image in the cloud, but what we need to do is to make the Docker building even more efficiency. Make the computation 100% stable so we can utilize the huge computational power in the cloud. Also the price, so the key here is the price. So if we have one setup in the cloud, a data center for example, we currently maintain two data centers one across Europe, another is in United States. So if we have one data center and 50 hospitals using it every day, then we need the numbers. The average price for one patient comes to a few dollars per patient. So if we consider this medical health care system the costs, the ideal costs of using cloud computing can be truly trivial, but what we can offer to patients and doctor has never happened. The computation you can bring to us is something they never saw before and they never experienced. So I believe that's the future, it's not, the old model is everyone has his own computational server, then maintaining that, it costs a lot of work. Even doctor make the software aspects much easier, but the hardware, someone still need to set-up them. But using cloud will change all of. So I think the next future is definitely to wholly utilize the cloud with the container technology. >> Excellent. Well, we thank you so much. I know everyone appreciates the work your team's doing and absolutely if things can be done to allow scalability and lower cost per patient that would be a huge benefit. Thank you so much for joining us. >> Thank you. >> All right, stay tuned for lots more coverage from theCUBE at DockerCon Live 2020. I'm Stu Miniman and thank you for watching theCUBE. (gentle music)

>> Announcer: From Las Vegas, it's theCUBE. Covering Qualys Security Conference 2019, brought to you by Qualys. >> Hey welcome back everybody, Jeff Frick here with theCUBE, we're in Las Vegas at the Bellagio, at the Qualys Security Conference, pretty amazing, it's been going on for 19 years, we heard in the keynote. It's our first time here, and we're excited to have our first guest, he was a keynote earlier this morning, the author of nine books, Richard Clarke, National Security and Cyber Risk expert, and author most recently of "The Fifth Domain." Dick, great to see you. >> Great to be with you. >> Absolutely. So you've been in this space for a very long time. >> I started doing cybersecurity in about 1996 or 1997. >> So early days. And preparing for this, I've watched some of your other stuff, and one of the things you said early on was before there was really nothing to buy. How ironic to think about that, that first there was a firewall, and basic kind of threat protection. Compare and contrast that to walking into RSA, which will be in a couple of months in Moscone, 50,000 people, more vendors than I can count on one hand, now there's too much stuff to buy. Do you look at this evolution? What's your take? And from a perspective of the CIO and the people responsible for protecting us, how should they work through this morass? >> Well, the CIO and the CFO, got used to thinking cyber security costs a little bit, 'cause you can only buy, this is 1997, you can only buy antivirus, firewall, and maybe, in 1997, you could buy an intrusion detection system. Didn't do anything, it just went "beep," but you could buy that too. So you had three things in 1997. And so that resulted in the IT budget having to take a tiny little bit of it, and put it aside for security, maybe 2%, 3% of the budget. Well, now, if you're only spending 2 or 3% of your IT budget on security, somebody owns your company, and it's not you (laughs). >> And that's 2 or 3% of the IT budget, that's not the whole budget. >> No, that's the IT budget. What we found in researching the book, is that secure companies, and there are some, there's companies that don't get hacked, or they get hacked, but the hack gets in, immediately contained, identified, quarantined. The damage is done, but it's easily repaired. Companies that are like that, the resilient companies, are spending 8%, 10%, we found companies at 12 and 17%, of their IT budget on security, and to your point, how many devices do you have to buy? You look at the floor at any of these RSA Conventions, Black Hat, or something, now there are 2000 companies at RSA, and they're all selling something, but their marketing message is all the same. So pity the poor CSO as she goes around trying to figure out, "Well, do I want to talk to that company? "What does it do?" We found that the big banks, and the big corporations, that are secure, have not three, anymore, but 75, 80, different, discreet cybersecurity products on their network, most of it software, some of it hardware. But if you've got 80 products, that's probably 60 vendors, and so you got to, for yourself, there's the big challenge, for a CSO, she's got to figure out, "What are the best products? "How do they integrate? "What are my priorities?" And, that's a tough task, I understand why a lot of the people want to outsource it, because it's daunting, especially for the small and medium-size business, you got to outsource it. >> Right, right. So the good news is, there's a silver lining. So traditionally, and you've talked about this, we talk about it all the time too, there's people that have been hacked and know it, and people that have been hacked and just don't know it yet, and the statistics are all over the map, anywhere you grab it, it used to be hundreds of days before intrusions were detected. Kind of the silver lining in your message is, with proper investments, with proper diligence and governance, you can be in that group, some they're trying to get in all the time, but you can actually stop it, you can actually contain it, you can actually minimize the damage. >> What we're saying is, used to be two kinds of companies, those that are hacked and knew it, and those that are hacked that don't, that didn't know it. Now there's a third kind of company. The company that's stopping the hack successfully, and the average, I think, is a 175 days to figure it out, now it's 175 minutes, or less. The attack gets in, there's all the five or six stages, of what's called "the attack killchain," and gets out very, very quickly. Human beings watching glass, looking at alerts, are not going to detect that and respond in time, it's got to be automated. Everybody says they got AI, but some people really do (laughs), and machine learning is absolutely necessary, to detect things out of the sea of data, 75 different kinds of devices giving you data, all of them alarming, and trying to figure out what's going on, and figure out in time, to stop that attack, quarantine it, you got to move very, very quickly, so you've got to trust machine learning and AI, you got to let them do some of the work. >> It's so funny 'cause people still are peeved when they get a false positive from their credit card company, and it's like (laughs), do you realize how many of those things are going through the system before one elevates to the level that you are actually getting an alert? >> So the problem has always been reducing the number of false positives, and identifying which are the real risks, and prioritizing, and humans can't do that anymore. >> Right, right, there's just too much data. So let's shift gears a little bit about in terms of how this has changed, and again, we hear about it over and over, right, the hacker used to be some malicious kid living in his mom's basement, being mischievous, maybe, actually doing some damage, or stealing a little money. Now it's government-funded, it's state attacks, for much more significant threats, and much more significant opportunities, targets of opportunity. You've made some interesting comments in some of your prior stuff, what's the role of the government? What's the role of the government helping businesses? What's the role of business? And then it also begs the question, all these multinational business, they don't even necessarily just exist in one place, but now, I've got to defend myself against a nation state, with, arguably, unlimited resources, that they can assign to this task. How should corporate CIOs be thinking about that, and what is the role, do you think, of the government? >> Let's say you're right. 20 years ago we actually used to see the number of cyber attacks go up on a Friday night and a Saturday night, because it was boys in their mother's basement who couldn't get a date, you know, and they were down there having fun with the computer. Now, it's not individuals who are doing the attacks. It is, as you say, nation states. It's the Russian Army, Russian Intelligence, Russian Military Intelligence, the GRU. The North Korean Army is funding its development of nuclear weapons by hacking companies and stealing money, all over the world, including central banks, in some cases. So, yeah, the threat has changed, and obviously, a nation state is going to be far more capable of attacking, military is going to be far more capable of attacking, so, CISOs say to me, "I'm being attacked by a foreign military, "isn't that the role of the Pentagon "to defend Americans, American companies?" And General Keith Alexander, who used to run Cyber Command, talks about, if a Russian bomber goes overhead, and drops a bomb on your plant, you expect the United States Air Force to intercept that Russian bomber, that's why you pay your taxes, assuming you pay taxes. What's the difference? General Alexander says, whether that's a Russian bomber attacking your plant, or a Russian cyber attack, attacking your plant, and he says, therefore, people should assume the Pentagon will protect them from foreign militaries. That sounds nice. There's a real ring of truth to that, right? But it doesn't work. I mean, how could the Pentagon defend your regional bank? How could the Pentagon defend the telephone company, or a retail store? It can't. It can barely defend itself, and they're not doing a great job of that either, defending the federal government. So, do you really want the Pentagon putting sensors on your network? Looking at your data? No, you don't. Moreover, they can't. They don't have enough people, they don't have enough skills. At the end of the day, whatever the analogy is about how the Defense Department should defend us from foreign military attack, they can't. And they shouldn't, by the way, in my view. The conclusion that that gets you to, is you got to defend yourself, and you can, right now, if you use the technology that exists. The government has a role, sure. It can provide you warnings, it can provide the community with intelligence, it can fund development and stuff, can train people, but it cannot defend your network, you have to defend your network. >> And you have municipalities, I think it's Atlanta, is the one that keeps getting hit, there's-- >> Well Louisiana, just the other night, the whole state of Louisiana government unplugged from the internet, because it was being hit by a ransomware attack. The whole city of Baltimore's been down, the whole city of Atlanta, as you said. There's a real problem here, because people, many of them are paying the ransom, and they pay the ransom, and they get their network back right away. People ask me, "Can I trust these criminals?" Well you can trust them to give you your network back, because they have a reputation to maintain. Think about that. This whole thing about ransomware depends on their reputation, the bad guys' reputation. If they get a reputation for not giving you your network back when you pay, no one's ever going to pay, so they do give it back, and sometimes that's a lot quicker, and a lot cheaper, than saying no and rebuilding your network. But if we give them the money, what are they doing with it? Yeah, they're buying Ferraris to drive round the streets of Moscow, but some of that money is going back into R&D, so they can develop more effective attacks. >> So it's an interesting take, right, so most people, I think, would say that the cybersecurity war is completely always going to be kind of cat and mouse, whack-a-mole, that the bad guys are always a little step ahead, and you're always trying to catch up, just the way the innovation cycle works. You specifically say no, that's not necessarily always true, that there are specific things you can do to, not necessarily have an impenetrable wall, but to really minimize the impact and neutralize these threats, like a super white blood cell, if you will. So what are those things that companies should be doing, to better increase their probability, their chance, of, I don't know, blocking-- >> Depends on the size of the company. >> Absorbing. >> Depends on the size of the company. But I think whether you're a small-to-medium business, or you're an enterprise, you begin in the same place. And I do this with all of my consulting contracts, I sit down with the leadership of the company individually, and I ask every one of them, "What are you worried about? "What could happen? "What could a bad guy do to you "that matters to your company?" 'Cause what matters to one company may not matter to another company. And you can't spend your entire budget defending the network, so let's figure out exactly what risk we're worried about, and what risk we're just kind of willing to tolerate. And then, we can design security around that, and sometimes that security will be outsourced, to a managed security provider. A lot of it means getting into the cloud, because if you're in Amazon or Microsoft's cloud, you've got some security automatically built in, they've got thousands of people doing the security of the cloud, and if your server's in your basement, good luck. (laughs) >> So, as you look forward, now you said you finished the book earlier in the year, it gets published, and it's out, and that's great, but as you said, it's a fast-moving train, and the spaces develops. 10 years from now, we don't want to look at 10 years from now, it's way too long. But as you look forward the next couple, two, three years, what are you keeping an eye on, that's going to be, again, another sea change of both challenge and opportunity in this space? >> The three technologies we talk about in the book, for the three-year time horizon, 'cause I can't get beyond three years, more machine learning on the defense, but also more machine learning on the offense, and where does that balance work out? To whose advantage? Secondly, quantum computing, which, we don't know how rapidly quantum computing will come onto the market, but we do know it's a risk for some people, in that it might break encryption, if the bad guys get their hands on the quantum computer, so that's a worry. But one I think most immediately, is 5G. What 5G allows people to do, is connect millions of things, at high speed, to the internet. And a lot of those things that will be connected are not defended right now, and are outside firewalls, and don't have end-point protection, and aren't really built into networks on a secure network. So I worry about 5G empowering the Internet of Things, and doing what we call expanding the attack surface, I worry about that. >> Right, Richard, well thank you for taking a few minutes, and congrats on the book, and I'm sure within a couple of years the gears will start turning and you'll put pen to paper and kick another one out for us. >> Number 10. >> All right. He's Richard, I'm Jeff, you're watching theCUBE, we're at the Qualys Security Conference at the Bellagio in Las Vegas, thanks for watching, we'll see you next time. (upbeat music)

>> from Seattle Washington. It's the Q covering AWS. Imagine nonprofit brought to you by Amazon Web >> service is >> Hey, welcome back already. Jeffrey here with the Cube were in >> the waterfront, actually in Seattle, Washington. It's an absolutely gorgeous August day. We're here for the AWS. Imagine nonprofit event. It's the fourth year they've had. It is the first year's been kind of open to the public. It was invitation only. And we're excited to be here for our first time. Our >> guest is here for his first time, too. And >> we're excited to sit down with side L. Nash. He is the vice president. And of I t and C i o for the National Council on Aging. Say great to see you. >> Thank you. Good to see you. Yeah. So, first >> off, just kind of impressions on the event So far. Really good keynotes this morning. And they got a full two days planned for you. >> Yes, it was an excellent good note. Keynote speaks to the speech this morning and, uh, started off talking about impact and how nonprofit organizations make it make a difference in the world. >> Right. So National Council of Aging, the population is aging Maur Every day they keep sending me my my card in the mail that keep pretending I'm not old enough to get. But >> don't try to pretend exactly they are >> double AARP. Thank you very much for the car, but, um, there's a lot of unique challenges with as the population continues to get holding. What are some of your organisation's priorities? How do you address this kind of growing population in our society? >> So I'll share with you some statistics on aging. So there are about 72,000,060 and older adults in the U. S. 70 >> 1,000,000 to three on its growing >> and growing. It will be 92,000,000 in 2030. So it's a growing larger segment of the population. People are living longer, saving less about but half of those so are 60 plus have saving off about $30,000 about 80% off 60 plus have about maybe to chronic disease conditions. So people are living longer, saving less money, and obviously with that, there are a lot of challenges, and this is where we step in. So we step in. Our mission is to help people age healthier and wealthier, try to make sure that they planned correctly for their savings. And they plan correctly also for their convention there chronic diseases and managing their health in general. And so for that, we have a lot off just products, actually that help older adults figuring out there how to live in older and healthy life. One of them is our flagship product, helping people get access to ah, federal, state and local government benefits. It's called benefits. Checkup is the largest system decision support system in the country that helps older adults figuring out how what benefits take all 54 and how to apply. And we walked them through that whole process. >> So it's also not necessarily the most technically astute population, either, especially today seniors who didn't grow up his digital natives like a lot of the kids are today. And >> as you said, your your guys >> objective number one is economic security. Maybe not necessarily number one, but top of the list and then healthy living. And they don't have the benefit of of time for therefore one case and stuff to grow. So these air pretty unique challenges. How are you helping him? And then you know we're here in eight of us. What role has eight of us played in helping you reach your your constituent? >> Clear? You're asking a lot of questions in one. So let me try and answer them one by one. So let's take a >> look at the aging population, especially the older adults. 70 plus those who actually don't have. Ah, I don't know. They're not necessity technology savvy, but they have Ah, they have cell phone. It's over. 73% of them have cell phones and some have smartphones. S o. We looked at the different ways of trying to reach out to them. And one of the things that we experimented with is looking at an SMS texting pilot. So we actually started that pilot and was very successful. And well, now we're rolling out into a full production system. It's a we found out that it's a great channel. It's very simple asking simple questions. Did you apply yes or no? Just answer us if you were to do one or two. So tell us give us a very simple answer and we found that the engagement rates are way above the average industry. People tend to respond to text messages for better than actually telling them. Hey, there's the mobile app. Go download my mobile already So that's one aspect of it on the AWS Sod off it. So when I joined and see away about a year and 1/2 ago, we were in Private Cloud and in that situation we had a lot of single point of failures and disaster recovery was in bad shape. And so we realized that we needed to move into a new and more robust environment, one that solved the single all the risks that we had from disaster recovery. Single point of failures to also being able to innovate quickly and fast. And so we looked that we started the ah migration process to the cloud and we ended up on AWS back in February. This year would move 95% of our assets to the cloud to AWS Cloud and we medicated the two major risks. The single point of failure is disaster recovery and so on. And with that, we also have a lot of other tools that are out of the box that we're using right now with the AWS platform. >> That's great So, um, I want it back up to the S, the best comic cause That's really interesting. So how do you find your customers? How do you get people get engaged? Obviously, art center the card in the mail. You know, there's a lot of organizations that that we get involved with. How do you directly engage with your clients? >> So we do a lot of digital marketing, believe it or not. So we spent a lot off time money and energy into digital marketing on Facebook. So a good number of older adults are on Facebook. There's also a good percentage of them that are on YouTube. Unfortunately, older adults spend about 46 hours watching either TV or videos on the Web, those who have access to the Web. So that's one way we're trying to reach them. So these are our sort of marketing funnels. In addition to that, we have about close to 100 centers around the U. S. Where older those can actually go in and be helped and go walk through the process of applying for federal state local government benefits. And so we have. They're called benefits benefits centers. And so the those centers are open to the public. We also try to collaborate with different with different organizations around the country, through through whom we get older adults too engage with us and joined the benefits checkup program. And with that, we we ask people to our 10. So we take a very cautious and very respectful approach to data and privacy to ask people to opt in. And we tell them about how we're using the data. We encrypt the data address. We take very caring very good care of it. We don't share it outside of organization. So we have our own internal data privacy principles. So we take this matter very seriously again. Our objectives always the hope older adults live a better, healthier and wealthier life, >> right? I just love that the older people are now using Facebook and SMS like kids. >> 15 years ago, they moved on >> to other platforms. Thank goodness for the old folks keeping the Facebook and, uh >> so let's shift gears. A little >> bit of talk about your transformation in your movement to the cloud. How big of an effort was that? How long did it take? And, you know, hasn't really opened up the innovation because there's clearly cost savings. And as you talked about a single point of failure and kind of mitigating the negatives, but as well as we've seen over and over again, really, the benefits from from Cloud are really that innovation and delivering service is faster. So how's your experience? >> That's exactly right. So So let me talk a little bit about the traditional transformation. So about, I would say, year over year ago, we started our digital transformation initiative. It's really focused on customers, we call it, knowing our customers as individuals with individual needs. Traditionally organizations like ours looked at older adults. In the perspective, off percentages averages, on average is is how old they are on average, in this is their income. On average, this is their health. But in reality, every older adult is an individual that has specific and individual needs, and we need to really take a look at that and caters to those very specific needs that they actually changed over time. So the transformation really enabled it. We needed to move to a cloud where we can have products immediately that we can spend off and use a I machine learning products and so on. And so I'm gonna go back and talking more about our a digital transformation and the perspectives off it. So our objective long term is to build was recalling the the aging Well, aye aye. Engine. It's basically imagine an older adult waking up in the morning and trying to decide what are the top best three things for me to do. Stop the actions for me to do to improve my life. And we wanna help that older adult make those decisions easily and quickly through a frictionless interactions. Frictionless. Conversational. Aye, aye. Speaking to an Alexa like voice enabled smart speaker asking Alexa, what should I do today? Alexis, respond. The weather is nice out there. Call your friend. Go for a walk. Call your doctor, get the lab results and so on. And check your benefits on benefits. Check up and figure out and improve your life. So the idea is to really get the person to actively and the actively using technology and simple, frictionless way to be able to make those decisions that improve their lives. So for us to do this kind of work to build the aging. Well, Aye, aye. Engine. It is impossible without being on a cloud like >> a w. Interesting. So, uh, first time I've heard about Lexus since we've been here. A lot of talk about Lex at the education conference a couple of weeks back. So is Alexa. Pretty key piece of your strategy going forward, you really see voice as a different type of communication. You mentioned. That's a message. Just kind of old, but really effective. How do you see Alexa playing >> so absolutely so voice enabled communication channels. So we look at it as actually we look at our communication with older adults. We look at it as an Army channel communication. Every person have their own preference of the way they interact with technology. Some people prefer SMS. Others like to speak to Alexa. Others like to go through the web and so on. Some are on Facebook or YouTube, etcetera. So each we have our own choices. And that's exactly why we need to look at the older adults as individuals with their individual needs. And then our job is to deliver those to deliver all products through those different channels individually. So delivering the right product with the right customer at the right time and through the right channels. So lax is one of the channels it is. It's not the only channel or the voice channel I would call it is not the only channels. What we found out is that older adults find Alexis is very engaging. It reduces social isolation. It helps with the many other tests, especially for those who are visually inferred. The the complexity. The challenge for older adults is setting it up, so that's what we're trying to look at. Ways of trying to packages will be package so that it is possible for the older adult to plug it in and be able to use it. The other thing that we discovered, we probably need to look at family caregivers as the customer segment of the customer target that we would work with really enable looks, um, >> interesting. Let's see, it seems like a natural fit once you get kind of the tone and the and the comfort worked out, and I would imagine you're writing all types of specific things for to do and types of activities for Alexa to do for the specific needs of this older generation, >> so yeah. So we started >> a very small proof of concept project with Alexa trying to engage an experiment for me, everything that we do has to bring in value. And I need to also make sure that we are when we deliver a product or customers. That product actually delivers that value and engages the customers. So we know that there are there is the value in there were also working with partners on delivering this voice channel. So I know that we have, as a non profit organization with our, you know, a limited resource is. And so we look at partners as a way to enable those votes channels on the different channels that we have >> exciting, exciting times. And I look forward to watching that innovation pulls out at a high rate of speed. So thanks for taking a few minutes and safe travels home. >> Okay. Thank you, Seed. I'm Jeff. You're watching the keyboard aws. Imagine >> in Seattle. Thanks for watching. We'll see you next time

>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen brought to you by bread. Welcome back a few, but our way Our red have some twenty nineteen >> center along with Sue Mittleman. I'm John Walls were now joined by Robin Goldstone, who's HBC solution architect at the Lawrence Livermore National Laboratory. Hello, Robin >> Harrier. Good to see you. I >> saw you on the Keystone States this morning. Fascinating presentation, I thought. First off for the viewers at home who might not be too familiar with the laboratory If you could please just give it that thirty thousand foot level of just what kind of national security work you're involved with. >> Sure. So yes, indeed. We are a national security lab. And you know, first and foremost, our mission is assuring the safety, security reliability of our nuclear weapons stockpile. And there's a lot to that mission. But we also have broader national security mission. We work on counterterrorism and nonproliferation, a lot of of cyber security kinds of things. And but even just general science. We're doing things with precision medicine and and just all all sorts >> of interesting technology. Fascinating >> Es eso, Robin, You know so much and i t you know, the buzzword. The vast months years has been scaled on. We talk about what public loud people are doing. It's labs like yours have been challenged. Challenge with scale in many other ways, especially performance is something that you know, usually at the forefront of where things are you talked about in the keynote this morning. Sierra is the latest generation supercomputer number two, you know, supercomputer. So you know, I don't know how many people understand the petaflop one hundred twenty five flops and the like, but tell us a little bit about, you know, kind of the why and the what of that, >> right? So So Sierra's a supercomputer. And what's unique about these systems is that we're solving. There's lots of systems that network together. Maybe you're bigger number of servers than us, but we're doing scientific simulation, and that kind of computing requires a level of parallelism and very tightly coupled. So all the servers are running a piece of the problem. They all have to sort of operate together. If any one of them is running slow, it makes the whole thing goes slow. So it's really this tightly couple nature of super computers that make things really challenging. You know, we talked about performance. If if one servers just running slow for some reason, you know everything else is going to be affected by that. So we really do care about performance. And we really do care about just every little piece of the hardware you know, performing as it should. So So I >> think in national security, nuclear stockpiles. Um I mean, there is nothing more important, obviously, than the safety and security of the American people were at the center of that. Right? You're open source, right? You know, how does that work? How does that? Because as much trust and faith and confidence we have in the open source community. This is an extremely important responsibility that's being consigned more less to this open source community. >> Sure. You know, at first, people do have that feeling that we should be running some secret sauce. I mean, our applications themselves or secret. But when it comes to the system software and all the software around the applications, I mean, open source makes perfect sense. I mean, we started out running really closed source solutions in some cases, the perp. The hardware itself was really proprietary. And, of course, the vendors who made the hardware proprietary. They wanted their software to be proprietary. But I think most people can resonate when you buy a piece of software and the vendor tells you it's it's great. It's going to do everything you needed to do and trust us, right? Okay, But at our scale, it often doesn't work the way it's It's supposed to work. They've never tested it. Our skill. And when it breaks, now they have to fix. They're the only ones that can fix it. And in some cases we found it wasn't in the vendors decided. You know what? No one else has one quite like yours. And you know, it's a lot of work to make it work for you. So we're just not going to fix and you can't wait, right? And so open source is just the opposite of that, right? I mean, we have all that visibility in that software. If it doesn't work for our needs, we can make it work for our needs, and then we can give it back to the community. Because even though people are doing things that the scale that we are today, Ah, lot of the things that we're doing really do trickle down and can be used by a lot of other people. >> But it's something really important because, as you said, you used to be and I was like, OK, the Cray supercomputer is what we know, You know, let's use proprietary interfaces and I need the highest speed and therefore it's not the general purpose stuff. You moved X eighty six. Lennox is something that's been in the shower computers. Why? But it's a finely tuned version there. Let's get you know, the duct tape and baling wire. And don't breathe on it once you get it running. You're running well today and you talk a little bit about the journey with Roland. You know, now on the Super Computers, >> right? So again, there's always been this sort of proprietary, really high end supercomputing. But about in the late nineteen nineties, early two thousand, that's when we started building these these commodity clusters. You know, at the time, I think Beta Wolf was the terminology for that. But, you know, basically looking at how we could take these basic off the shelf servers and make them work for our applications and trying to take advantage of a CZ much commodity technologies we can, because we didn't want to re invent anything. We want to use as much as possible. And so we've really written that curve. And initially it was just red hat. Lennox. There was no relative time, but then when we started getting into the newer architectures going from Mexico six. Taxi, six, sixty for and Itanium, you know the support just wasn't there in basic red hat and again, even though it's open source and we could do everything ourselves, we don't want to do everything ourselves. I mean, having an organization having this Enterprise edition of Red Hat having a company stand behind it. The software is still open. Source. We can look at the source code. We can modify it if we want, But you know what at the end of the day, were happy to hand over some of our challenge is to Red Hat and and let them do what they do best. They have great, you know, reach into the into the colonel community. They can get things done that we can't necessarily get done. So it's a great relationship. >> Yes. So that that last mile getting it on Sierra there. Is that the first time on one kind of the big showcase your computer? >> Sure. And part of the reason for that is because those big computers themselves are basically now mostly commodity. I mean, again, you talked about a Cray, Some really exotic architecture. I mean, Sierra is a collection of Lennox servers. Now, in this case, they're running the power architecture instead of X eighty six. So Red hat did a lot of work with IBM to make sure that that power was was fully supported in the rail stack. But so, you know, again that the service themselves somewhat commodity were running and video GP use those air widely used everywhere. Obviously big deal for machine learning and stuff that the main the biggest proprietary component we're still dealing was is thie interconnect. So, you know, I mentioned these clusters have to be really tightly coupled. They that performance has to be really superior and most importantly, the latent see right, they have to be super low late and see an ethernet just doesn't cut it >> So you run Infinite Band today. I'm assuming we're >> running infinite band on melon oxen finna ban on Sierra on some of our commodity clusters. We run melon ox on other ones. We run intel. Omni Path was just another flavor of of infinite band. You know, if we could use it, if we could use Ethernet, we would, because again, we would get all the benefit in the leverage of what everybody else is doing, but just just hasn't hasn't quite been able to meet our needs in that >> area now, uh, find recalled the history lesson. We got a bit from me this morning. The laboratory has been around since the early fifties, born of the Cold War. And so obviously open source was, you know? Yeah, right, you know, went well. What about your evolution to open source? I mean, ahs. This has taken hold. Now, there had to be a tipping point at some point that converted and made the laboratory believers. But if you can, can you go back to that process? And was it of was it a big moment for you big time? Or was it just a kind of a steady migration? tour. >> Well, it's interesting if you go way back. We actually wrote the operating systems for those early Cray computers. We wrote those operating systems in house because there really was no operating system that will work for us. So we've been software developers for a long time. We've been system software developers, but at that time it was all proprietary in closed source. So we know how to do that stuff. The reason I think really what happened was when these commodity clusters came along when we showed that we could build a, you know, a cluster that could perform well for our applications on that commodity hardware. We started with Red Hat, but we had to add some things on top. We had to add the software that made a bunch of individual servers function as a cluster. So all the system management stuff the resource manager of the thing that lets a schedule jobs, batch jobs. We wrote that software, the parallel file system. Those things did not exist in the open source, and we helped to write those things, and those things took on lives of their own. So luster. It's a parallel file system that we helped develop slow, Erm, if anyone outside of HBC probably hasn't heard of it, but it's a resource manager that again is very widely popular. So the lab really saw that. You know, we got a lot of visibility by contributing this stuff to the community. And I think everybody has embracing. And we develop open source software at all different layers. This >> software, Robin, you know, I'm curious how you look at Public Cloud. So, you know, when I look at the public odd, they do a lot with government agencies. They got cloud. You know, I've talked to companies that said I could have built a super computer. Here's how long and do. But I could spend it up in minutes. And you know what I need? Is that a possibility for something of yours? I understand. Maybe not the super high performance, But where does it fit in? >> Sure, Yeah. I mean, certainly for a company that has no experience or no infrastructure. I mean, we have invested a huge amount in our data center, and we have a ton of power and cooling and floor space. We have already made that investment, you know, trying to outsource that to the cloud doesn't make sense. There are definitely things. Cloud is great. We are using Gove Cloud for things like prototyping, or someone wants a server, that some architecture, that we don't have the ability to just spin it up. You know, if we had to go and buy it, it would take six months because you know, we are the government. But be able to just spin that stuff up. It's really great for what we do. We use it for open source for building test. We use it to conferences when we want to run a tutorial and spin up a bunch of instances of, you know, Lennox and and run a tutorial. But the biggest thing is at the end of the day are our most important work. Clothes are on a classified environment, and we don't have the ability to run those workloads in the cloud. And so to do it on the open side and not be ableto leverage it on the close side, it really takes away some of the value of because we really want to make the two environments look a similar is possible leverage our staff and and everything like that. So that's where Cloud just doesn't quite fit >> in for us. You were talking about, you know, the speed of, Of of Sierra. And then also mentioning El Capitan, which is thie the next generation. You're next, You know, super unbelievably fast computer to an extent of ten X that off current speed is within the next four to five years. >> Right? That's the goal. I >> mean, what those Some numbers that is there because you put a pretty impressive array up there, >> right? So Series about one hundred twenty five PETA flops and are the big Holy Grail for high performance computing is excess scale and exit flop of performance. And so, you know, El Capitan is targeted to be, you know, one point two, maybe one point five exit flops or even Mohr again. That's peak performance. It doesn't necessarily translate into what our applications, um, I can get out of the platform. But the reason you keep sometimes I think, isn't it enough isn't one hundred twenty five five's enough, But it's never enough because any time we get another platform, people figure out how to do things with it that they've never done before. Either they're solving problems faster than they could. And so now they're able to explore a solution space much faster. Or they want to look at, you know, these air simulations of three dimensional space, and they want to be able to look at it in a more fine grain level. So again, every computer we get, we can either push a workload through ten times faster. Or we can look at a simulation. You know, that's ten times more resolved than the one that >> we could do before. So do this for made and for folks at home and take the work that you do and translate that toe. Why that exponential increase in speed will make you better. What you do in terms of decision making and processing of information, >> right? So, yeah, so the thing is, these these nuclear weapons systems are very complicated. There's multi physics. There's lots of different interactions going on, and to really understand them at the lowest level. One of the reasons that's so important now is we're maintaining a stockpile that is well beyond the life span that it was designed for. You know, these nuclear weapons, some of them were built in the fifties, the sixties and seventies. They weren't designed to last this long, right? And so now they're sort of out of their design regime, and we really have to understand their behaviour and their properties as they age. So it opens up a whole nother area, you know, that we have to be able to floor and and just some of that physics has never been explored before. So, you know, the problems get more challenging the farther we get away from the design basis of these weapons, but also were really starting to do new things like eh, I am machine learning things that weren't part of our workflow before. We're starting to incorporate machine learning in with simulation again to help explore a very large problem space and be ableto find interesting areas within a simulation to focus in on. And so that's a really exciting area. And that is also an area where, you know, GPS and >> stuff just exploded. You know, the performance levels that people are seeing on these machines? Well, we thank you for your work. It is critically important, azaz, we all realize and wonderfully fascinating at the same time. So thanks for the insights here on for your time. We appreciate that. >> All right, Thanks for >> thanking Robin Goldstone. Joining us back with more here on the Cube. You're watching our coverage live from Boston of Red Hat Summit twenty nineteen.

>> Narrator: Live from Orlando, Florida, it's theCUBE covering .conf18. Brought to you by Splunk. >> Welcome back to Orlando, everybody, of course home of Disney World. I'm Dave Vellante with Stu Miniman. We're here covering Splunk's Conf18, #conf, sorry, #splunkconf18, I've been fumbling that all week, Stu. Maybe by day two I'll have it down. But this is theCUBE, the leader in live tech coverage. Phillip Adams is here, he's the CTO and lead architect for the National Ignition Facility. Thanks for coming on. >> Thanks for having me. >> Super-interesting off-camera conversation. You guys are basically responsible for keeping the country's nuclear arsenal functional and secure. Is that right? >> Phillip: And effective. >> And effective. So talk about your mission and your role. >> So the mission of the National Ignition Facility is to provide data to scientists of how matter behaves under high pressures and high temperatures. And so what we do is basically take 192 laser beams of the world's largest laser in a facility about the size of three football fields and run that through into a target the size of a B.B. that's filled with deuterium and tritium. And that implosion that we get, we have diagnostics around that facility that collect what's going on for that experiment and that data goes off to the scientists. >> Wow, okay. And what do they do with it? They model it? I mean that's real data, but then they use it to model real-world nuclear stores? >> Some time back if you actually look on Google Earth and you look over Nevada you'll see a lot of craters in the desert. And we aren't able to do underground nuclear testing anymore, so this replaces that. And it allows us to be able to capture, by having a small burning plasma in a lab you can either simulate what happens when you detonate a nuclear warhead, you can find out what happens, if you're an astrophysicist, understand what happens from the birth of a star to full supernova. You can understand what happens to materials as they get subjected to, you know, 100 million degrees. (laughs) >> Dave: For real? >> Phillip: For real. >> Well, so now some countries, North Korea in particular, up until recently were still doing underground testing. >> Correct. >> Are you able to, I don't know, in some way, shape or form, monitor that? Or maybe there's intelligence that you can't talk about, but do you learn from those? Or do you already know what's going on there because you've been through it decades ago? >> There are groups at the lab that know things about things but I'm not at liberty to talk about that. (laughs) >> Dave: (chuckles) I love that answer. >> Stu: Okay. >> Go ahead, Stu. >> Maybe you could talk a little bit about the importance of data. Your group's part of Lawrence Livermore Labs. I've loved geeking out in my career to talk to your team, really smart people, you know, some sizeable budgets and, you know, build, you know, supercomputers and the like. So, you know, how important is data and, you know, how's the role of data been changing the last few years? >> So, data's very critical to what we do. That whole facility is designed about getting data out. And there are two aspects of data for us. There's data that goes to the scientists and there's data about the facility itself. And it's just amazing the tremendous amount of information that we collect about the facility in trying to keep that facility running. And we have a whole just a line out the door and around the corner of scientists trying to get time on the laser. And so the last thing IT wants to be is the reason why they can't get their experiment off. Some of these experimentalists are waiting up to like three, four years to get their chance to run their experiment, which could be the basis of their scientific career that they're studying for that. And so, with a facility that large, 66 thousand control points, you can consider it 66 thousand IOT points, that's a lot of data. And it's amazing some days that it all works. So, you know, by being able to collect all that information into a central place we can figure out which devices are starting to misbehave, which need servicing and make sure that the environment is functional as well as reproducible for the next experiment. >> Yeah well you're a case-in-point. When you talk about 66 thousand devices, I can't have somebody going manually checking everything. Just the power of IOT, is there predictive things that let you know if something's going to break? How do you do things like break-fix? >> So we collect a lot of data about those end-point devices. We have been collecting them and looking at that data into Splunk and plotting that over time, all the way from, like, capacitors to motor movements and robot behavior that is going on in the facility. So you can then start getting trends for what average looks like and when things start deviating from norm and set a crew of technicians that'll go in there on our maintenance days to be able to replace components. >> Phillip what are you architecting? Is it the data model, kind of the ingest, the analyze, the dissemination, the infrastructure, the collaboration platform, all of the above? Maybe you could take us inside. >> I am the infrastructure architect, the lead infrastructure architect, so I have other architects that work with me, for database, network, sys admin, et cetera. >> Okay, and then so the data, presumably, informs what the infrastructure needs to looks like, right, i.e. where the data is, is it centralized, de-centralized, how much is it, et cetera. Is that a fair assertion? >> I would say the machine defines what the architecture needs to look like. The business processes change for that, you know, in terms of like, well how do you protect and secure a SCADA environment, for example. And then for the nuances of trying to keep a machine like that continually running and separated and segregated as need be. >> Is what? >> As need be. >> Yeah, what are the technical challenges of doing that? >> Definitely, you know, one challenge is that the Department of Energy never really shares data to the public. And for, you know, it's not like NASA where you take a picture and you say, here you go, right. And so when you get sensitive information it's a way of being able to dissect that out and say, okay well now we've got to use our community of folks that now want to come in remotely, take their data and go. So we want to make sure we do that in a secure manner and also that protects scientists that are working on a particular experiment from another scientist working on their experiment. You know, we want to be able to keep swim lanes, you know, very separated and segregated. Then you get into just, you know, all of these different components, IT, the general IT environment likes to age out things every five years. But our project is, you know, looking at things on a scale of 30 years. So, you know, the challenges we deal with on a regular basis for example are protocols getting decommissioned. And not all the time because, you know, the protocol change doesn't mean that you want to spend that money to redesign that IOT device anymore, especially when you might have a warehouse full of them and then back-up, yeah. >> So obviously you're trying to provide access to those who have the right to see it, like you say, swim lanes get data to the scientists. But you also have a lot of bad guys who would love to get their hands on that data. >> Phillip: That's right. >> So how do you use, I presume you use Splunk at least in part in a security context, is that right? >> Yeah, we have a pretty sharp cyber security team that's always looking at the perimeter and, you know, making sure that we're doing the right things because, you know, there are those of us that are builders and there are those that want to destroy that house of cards. So, you know, we're doing everything we can to make sure that we're keeping the nation's information safe and secure. >> So what's the culture like there? I mean, do you got to be like a PhD to work there? Do you have to have like 15 degrees, CS expert? I mean, what's it like? Is it a diverse environment? Describe it to us. >> It is a very diverse environment. You've got PhD's working with engineers, working with you know, IT people, working with software developers. I mean, it takes an army to making a machine like this work and, you know, it takes a rigid schedule, a lot of discipline but also, you know, I mean everybody's involved in making the mission happen. They believe in it strongly. You know, for myself I've been there 15 years. Some folks have been there working at the lab 35 years plus, so. >> All right, so you're a Splunk customer but what brings you to .conf? You know, what do you look to get out of this? Have you been to these before? >> Ah yes, you know, so at .conf, you know, I really enjoy the interactions with other folks that have similar issues and missions that we do. And learning what they have been doing in order to address those challenges. In addition staying very close with technology, figuring out how we can leverage the latest and greatest items in our environment is what's going to make us not only successful but a great payoff for the American taxpayer. >> So we heard from Doug Merritt this morning that data is messy and that what you want to be able to do is be able to organize the data when you need to. Is that how you guys are looking at this? Is your data messy? You know, this idea of schema on read. And what was life like, and you may or may not know this, kind of before Splunk and after Splunk? >> Before Splunk, you know, we spent a lot of time in traditional data warehousing. You know, we spent a lot of time trying to figure out what content we wanted to go after, ETL, and put that data sets into rows and tables, and that took a lot of time. If there was a change that needed to happen or data that wasn't on-boarded, you couldn't get the answer that you needed. And so it took a long time to actually deliver an answer about what's going on in the environment. And today, you know one of the things that resonated with me is that we are putting data in now, throwing it in, getting it into an index and, you know, almost at the speed of thought, then being able to say, okay, even though I didn't properly on-board that data item I can do that now, I can grab that, and now I can deliver the answer. >> Am I correct that, I mean we talk to a lot of practitioners, they'll tell you that when you go back a few years, their EDW they would say was like a snake swallowing a basketball. They were trying to get it to do things that it really just wasn't designed to do, so they would chase intel every time intel came up with a new chip, hey we need that because we're starved for horsepower. At the same time big data practitioners would tell you, we didn't throw out our EDW, you know, it has its uses. But it's the right tool for the right job, the horses for courses as they say. >> Phillip: Correct. >> Is that a fair assessment? >> That is exactly where we're in. We're in very much a hybrid mode to where we're doing both. One thing I wanted to bring up is that the message before was always that, you know, the log data was unstructured content. And I think, you know, Splunk turned that idea on its head and basically said there is structure in log data. There is no such thing as unstructured content. And because we're able to rise that information up from all these devices in our facility and take relational data and marry that together through like DB Connect for example, it really changed the game for us and really allowed us to gain a lot more information and insight from our systems. >> When they talked about the enhancements coming out in 7.2 they talked about scale, performance and manageability. You've got quite a bit of scale and, you know, I'm sure performance is pretty important. How's Splunk doing? What are you looking for them to enhance their environment down the road, maybe with some of the things they talked about in the Splunk Next that would make your job easier? >> One of the things I was really looking forward to that I see that the signs are there for is being able to roll off buckets into the cloud. So, you know, the concept of being able to use S3 is great, you know, great news for us. You know, another thing we'd like to be able to do is store longer-lived data sets in our environment in longer time series data sets. And also annotate a little bit more, so that, you know, a scientist that sees a certain feature in there can annotate what that feature meant, so that when you have to go through the process of actually doing a machine-learning, you know, algorithm or trying to train a data set you know what data set you're trying to look for or what that pattern looks like. >> Why the S3, because you need a simple object store, where the GET PUT kind of model and S3 is sort of a de facto standard, is that right? >> Pretty much, yeah, that and also, you know, if there was a path to, let's say, Glacier, so all the frozen buckets have a place to go. Because, again, you never know how deep, how long back you'll have to go for a data set to really start looking for a trend, and that would be key. >> So are you using Glacier? >> Phillip: Not very much right now. >> Yeah, okay. >> There are certain areas my counterparts are using AWS quite a bit. So Lawrence Livermore has a pretty big Splunk implementation out on AWS right now. >> Yeah, okay, cool. All right, well, Phillip thank you so much for coming on theCUBE and sharing your knowledge. And last thoughts on conf18, things you're learning, things you're excited about, anything you can talk about. >> (laughs) No, this is a great place to meet folks, to network, to also learn different techniques in order to do, you know, data analysis and, you know, it's been great to just be in this community. >> Dave: Great, well thanks again for coming on. I appreciate it. >> Thank you. >> All right, keep it right there, everybody. Stu and I will be right back with our next guest. We're in Orlando, day 1 of Splunk's conf18. You're watching theCUBE.

>> Narrator: Live from Boston, Massachusetts. It's the Cube, covering ZertoCON 2018. Brought to you by Zerto. >> This is the Cube, I'm Paul Gillin, we're on the ground here in Boston for ZertoCON 2018 and joining me is Bill Manning who's in infrastructure operations at Woodforest National Bank. Now I was not familiar with Woodforest National Bank but I understand that regular visitors to WalMart in the south probably are. You're the WalMart bank I understand. >> That's what a lot of people like to call us. >> Your many branches are located in WalMarts in other words. And based in Houston, which has been no stranger to disasters lately >> Correct. >> The topic of IT resilience very much fresh on your mind. What is IT resilience mean in terms of your operations at Woodforest? >> We need to be very resilient in terms of natural disasters, hurricanes mostly. So, in order to prepare ourselves for that we migrate, 70% of our infrastructure between data centers every six months. When hurricane season starts we migrate away from Houston. When it's done, we migrate back. >> Now, why the migration strategy? Why move between data centers? Why not just settle on one data center that's out of harm's way if you will. >> Well there's no one data center that's out of 100% harm's way, so you need to make sure that if one data center goes down, you can always come up at your backup, or your primary data center. >> Now how did you become a Zerto customer? I understand you were one of their first, their first customer? >> We were their first customer we had Kashya before them and then RecoverPoint. Kashya was the precursor to Zerto. And whenever we were having issues with our replication appliance, we decided to look into Zerto, and we bought, implemented, and turned on Zerto fairly quickly. So we were the first customer and then we were the first customer that was using it. We actively utilized it to run a migration. And so far everything's going great. We love the product. And it works very well for us. >> Now being the first customer of a product is typically thought of as a risky proposition. What pushed you over the tipping point? >> We had an appliance that kept failing on us and the last failure was the straw, that broke the back. So we already had Zerto in a, I believe it was an alpha, possibly a beta test implementation, and when that straw finally broke, we turned off the appliance and we turned on Zerto. And it was very seamless. And yes there were headaches. We had issues with it. But a lot of the support tickets, all of the enhancement requests, a lot of those have our name on it. Because we utilized it. >> So you're doing the cloud migration every six months. What are some of the operational issues that you have to take into account when you're moving that size of processing load a couple hundred miles away? Or maybe Austin, maybe 100 miles away. >> We do it so often it's kind of second nature to us now. But we know the pain points of if you do it regularly, you know what happened last time. Hopefully you documented it. And you know what can happen this time. And a lot of times it's Firewall rules, it's what did we do at our current data center that we forgot to do at our other data center, in preparation for migration. So our biggest pain point is making sure we don't forget, oh hey we did something here, let's make sure to replicate it over and do the same thing over at our other data center. >> How has the role of backup changed over the time you've been using Zerto? It's not really, you don't have the luxury of point in time backups anymore. It's a continuous process, isn't it? >> Well we don't utilize Zerto for backups. We utilize another product for our primary backup system and we are a bank. We have seven year retention policies. So there are certain things that we have to keep on tape or on disk for a certain number of years. And Zerto doesn't immediately offer that to us. However we do utilize Zerto in a kind of pseudo backup process. If we need to recover a file that got deleted accidentally, I can either spend an hour using our other process or 10 minutes using Zerto. So we just pop into Zerto, use the journal file level recovery and there you go. >> You had, being in Houston you had a number of major storms in recent years. Are there any stories you can share with us about how you have managed to stay up and running during those storms? >> Our first storm, our first big storm right after Katrina was Rita. And when Rita came through, we didn't have what we have today. We ended up powering down non-critical items and making sure our critical applications were up and running. And luckily we didn't lose much power. We didn't lose any networking. Where as, during Harvey, we lost some networking for a week or two. The difference was we already moved everything to our secondary data center well away from the hurricane. And sure, one of our redundant paths was down. Our other one was up. We still had connectivity and we were doing great. So in terms of where we progress, hurricane season is what we are mainly concerned with. So we utilize Zerto, we move everything over. So if our data center, our primary data center in Houston goes down, we're mildly affected and customers shouldn't even notice. >> How does this make your business more resilient? I mean is this actually, is there business benefits to your, for your customers? >> Of course. >> Of the business being this resilient? >> If we're a bank and our ATMs go down, and we can't get them back up for a few days, our customers notice. If we're a bank and our primary systems go down and you can't take money out of your account for I believe the timeframe is 72 hours, the Federal government comes in and they own us now. We are no longer a bank. Because we didn't, we failed at providing services for our customers, for an extended period of time. And that's unacceptable. So to mitigate that we use a DR strategy. We use a business continuity plan. And we make sure that if something were to happen, even if it were outside of hurricane season, or if we were during hurricane season, and we had an issue at our other data center, Zerto allows us to bring everything back up within minutes. And because we do it regularly, if we're not going to have as many headaches as someone that just says, "Oh, well we've implemented Zerto but we don't utilize it." We run a few test failovers to make sure that we can actually migrate, but we don't bring anything up and run production load. We run production load every six months using Zerto. So that's how we get around making sure that we're highly available and we don't get taken over by the government. >> I hear a lot of talk, Bill, these days about digital transformation. How real is that to what Woodforest is doing? How are you changing the way you do business? >> I think it's already hard for us. I mean we've already gone digital. When I first started, we had couriers picking up paperwork from the branches and taking them to centralized processing locations, and running everything manually. Now it's all digitally. And that was partially thanks to 9/11. There was proof work they couldn't run for weeks because airports were down. And because of that banks started already going digital. So we already have digital transactions. Now if you write a check at WalMart, instead of taking a few days or a week or two to clear, it clears that day or the next day. Because it's all digital. WalMart went digital, we went digital. Most banks are already going digital or have already gone digital. So we just kind of, people ask, we're mostly already there. We're already digital. >> How about cloud? What's your road map when it comes to using multiple cloud providers? >> We're definitely looking into it, they give us a lot of benefit. They give us a lot of service that we can... >> You got a lot of flexibility. >> Flexibility, sure. Flexibility in doing things that we can't necessarily do ourselves. Right now we're taking baby steps. We're not throwing full production load into the cloud. We're looking at, let's put our development environment up there and see what it can provide for our developers. And so far they're enjoying what the opportunities or the possibilities can be. So we're looking forward to hopefully this year getting them up and running and in the cloud and enjoying all of the benefits from there. And after that once we get some development done in there, then we'll probably start seeing some production applications being put into the cloud. Some sort of probably SAS server offering. >> Well hurricane season is coming up in just a couple of months. I wish you the best >> Thank you so much. >> this season. Bill Manning thanks very much for joining us. >> Thank you very much, I appreciate it. >> We'll be right back from ZertoCON, I'm Paul Gillin, this is the Cube. (upbeat tech beats)

(soft click) >> Hey, welcome back everybody Jeff Frick here with theCUBE. We're at Data Privacy Day 2018 here at downtown San Francisco, the LinkedIn headquarters, gracious enough to hose this event. Bigger than last year, last year we were here for the first time at Twitter. And really the momentum continues to grow 'cause there's some big regulations coming down the pike that are really going to be into place. And have significant financial penalties, if you don't get your act together. So, we're excited to have the new Russell Schrader, the Executive Director of the National Cyber Security Alliance Organization behind this event. Russ, great to see you. >> Thank you very much for coming today, it was a great event. >> Absolutely, so, you've been on the job, this job, you said like less than two weeks. >> It's true. >> What do you think? I mean then they throw you right into the big event. >> Well, I've known the organization, I've known the event. But the staff really has done an outstanding job. They made it so easy for me, everything that they've done has just been terrific. They lined up fantastic speakers, they picked cutting edge topics, they put together a really well paced program, and it was just a terrific day for all of us to get in, really have some good discussions. >> You're off to a great start. (chuckles) >> Thank you. (both laugh) >> So you said you're familiar with your orginazation. You know, why are you here? Why did you take advantage of this opportunity? What do you kind of see as the role of this organization? And where do you see the opportunities to really make some significant impact going forward? >> Sure, the National Cyber Security Alliance is a who's who in the organization. People who really care about cyber security. Who see it as part of their social obligation. And it was a wonderful group that I'd worked with before. When I was at Visa and I see now, coming in as Executive Director, to really take it to the next level. We really are pushing, I think, on four separate areas that I think there's a lot of opportunity for it. Doing more cooperate work. Serving more consumers, more consumer education, more consumer awareness. I think working with educating staffers on the hill and in regulatory agencies in D.C. on changes and technological changes. And the cutting edge stuff. But in also, I think working academia, sort of getting involved and getting some of the scholarly, the cutting edge, the new ideas. And just preparing for what's going to happen in the next few years. >> Right, that's interesting 'cause you guys are National Cyber Security, security is often used as a reason to have less privacy. Right? It's often the excuse that the government, big brother, would used to say, you know, "We need to know what you're up to, we've got red light cameras all over the place to make sure you're not running red lights." So, it's an interesting relationship between privacy, security, and then what we're hearing more and more, really, a better linchpin to drive all this, which is, identity. So I wonder if you can share your kind of perspective on kind of the security versus privacy. Kind of trade off and debate. Or am I completely off base and they really need to run in parallel? >> Well, they do intersect a whole lot. People have talked about them being two sides of the same coin. Another speaker today said that security is a science but privacy is an art. As part of it is, you know, security is, the keeping the data in one place, the same way in as when you put it out. Sort of an integrity piece. You know, it isn't being misused, it's not being manipulated in a way and it's just not being changed. So that's a security piece. The privacy piece is people choosing what is used with that data. You know, is it to help me with an app? Is it to give me more information? Is it to give me games to play and things like that? So and that leads into a lot of different advantages in the web and on the internet. Now, identity since you put in a trifecta of big terms. >> Everything's got to be in threes, right? >> And there's three reasons for that. I think that, you know, the identity part is part of who are you. Now on the internet you can be a lot of people, right? The old cartoon was, you know, on the internet no one knows you're a dog. Well, on the internet, you can be a dog, you can be, you know, the person who you are at school, you can be the person who you are among your friends, you can be the person who you are at work. And those different selves, those different identities, are the internet of me. And we just need to make sure that you are curating your identities and sharing the information that you feel comfortable with. And that making sure that those are reaching the right people and not the wrong people. >> Right. So, there's an interesting kind of conundrum, we cover a lot of big data shows. And, you know, and there is kind of a fiduciary moral and now legal responsibility as you're collecting this data to drive some algorithm, some application that you know what you're using it for. And it's a good use of that. And you have a implicit agreement with the people providing you the data. But one the interesting things that comes up is then there's this thing where you've got that data and there's an application down the road that was not part of the original agreement. That no one even had an idea whatever happened. How does that fit in? Because as more and more of this data's getting stored. And there's actually a lot of value that can be unlocked, applying it in different ways, different applications. But, that wasn't the explicit reason that I gave it to you. >> Right, right. And that's really tricky because people have to be really vigilant. There is that education piece. That is the personal responsible piece to do business with companies and with apps that you feel comfortable with. But, you still have to trust but verify. And you do want to look into your phone, look into your PC, look into your other device. And figure out where things have changed, where things are moving. That's one of the great things about being in the Bay area today is innovation. But innovation, you just want to make sure that you are participating in it and you're in the part of innovation that's best for you. >> Okay, so, you mentioned academe, which is great, we do a lot of stuff at Stanford, we do a lot of stuff at MIT. So, as you look at kind of the academic opportunities. Kind of, where is some of the cutting edge research? Where are some of the academe focus areas that are helping advance the science of proxy? >> Well, you named two of the most forward thinking ones right there. So, I'll add to that just because we're talking about Stanford, we have to talk about Berkeley. >> Jeff: Yes. >> Right and Berkeley does have the whole group in privacy and law. On the east coast, in addition to MIT, you see George Washington is doing some things. George Mason is doing some things. And so you want to reach out to different areas. Cornell is doing things as well. So, we want to be able to figure out, where are the best ideas coming from? There are conferences already there. And maybe we can convene some papers, convene some people. And source out and give a little bit of more push and publish to people who otherwise wouldn't be getting the kind of publicity and encourage the kind of research. In privacy and in cyber security. Because there is the business and the consumer educational component. Not just, you know, the tech component to the academic work. >> So, before I let you go, last question. Where do you see is the biggest opportunity? Where's the biggest, either gap that needs to be filled, you know, kind of positive that's filling in negative, or an untapped positive that we've just barely scraped the surface of? >> Well, I think it's all about the consumer, to a large extent, to large one. You've got to figure out, how do you make your life easier. Right? Go back to the iPad introduction, nobody knew that they needed an iPad until they realized they couldn't live without it. You look at what's happened with mobile, right? Now, the idea of having a wallet, is on your phone. So, while I'm waiting in line at the grocery store, I'm checking my messages, I'm texting back and forth. And I just point my phone and I pay. Those kinds of areas are the kind of innovations that are consumer facing, that I think are really terrific. There's a lot of business work as well being done. But you have to figure out where that's going to go and I think the consumer just has a fantastic opportunity. >> Alright, well good opportunity, look forward to catching up a year from now and seeing how much progress you make. >> I think we had such a great program this year, I can't wait til next year, thank you. >> He's Russ Schrader, he's the Executive Director. I'm Jeff Frick, you're watching theCUBE, we're at Data Privacy Day 2018 in San Francisco. Thanks for watching, we'll catch you next time. (soft electronic music)

>> Announcer: Live from New Orleans, it's theCube. Covering VeeamOn 2017, brought to you by Veeam. >> We're back, Mahmoud Yassin is here, he's the Data Center Manager of the National Bank of Abu Dhabi. >> Good to see you. >> Nice to meet you. >> I'm Dave Vellante with my co-host Stu Miniman, this is theCube, the leader in live tech coverage. So, welcome to theCube. >> Thank you. >> Tell us about VeeamOn, what's your experience been like so far? >> Actually it is not the first time for me, I was here in the last few years. It's a very nice experience, mix it up with all IT companies, listening to people, leaders like Cisco, VMWare, Amazon, understanding what is the technology, how the market is moving, this is a great experience, great experience. >> Tell us about what's going on in the banking industry in your world in terms of the challenges and in particular, what's happening with the need for availability and uptime. >> I think you heard about what's happening last two days about Crayon, all the mobiles of all the IT people never stopped about the people and the need for restoring and restoring the machine cause of the people hit by crime virus or malware that encrypted the whole machine and you need to restore the data. This is happening all the time, there's not only banks in there, across different sections, three four days back, UK, the whole healthcare sector was stopped due to cyber attacks so availability and security has become so tight and of course banking means money, money is a big target for cyber-criminals. So we are caring about availability much much much more than before, it's not a matter of just fulfilling the checkbox and like what we have done in many years in IT, backup was a checkbox, just a checkbox, it's not anymore , no luxury to have the backup as a checkbox anymore. >> Checkbox meaning, okay I have-- >> I'm compliant, I have backup. >> We did it, we don't know if it failed or not but we did the backup. >> We did the backup, we tick it, we have the backup, we have the backup process, everything is okay. But have you never restored, restore in time, ensure the data availability is okay. How much data for how long? Regulation became as well very tough for the financial sector so you look at countries now that are asking for 15 to 20 years of data retention. So imagine your backups you need to maintain, multimedia, media is not sufficient anymore. Which tape drives will be sufficient for 15 to 20 years? After ten years, the tape will be rotten, it will not work, it will not be readable in the machine. And the data side as well is becoming a challenge so you need to offload the data to a desk, you need to offload the data to somewhere else that you ensure your availability and you need to ensure all that you can restore this data back. >> It's interesting you know, in IT we've known ransomware as something that's been discussed for a while, but now all of your customers know what it is. >> All of the customers, every one of them. >> So does that change the role? >> It's hitting the kids in the school, it's hitting everyone. >> You know, Dave's been talking for the last year that security has been like a board level discussion. Now it's your users, how much do your end users start coming and wanting to understand how secure money is. >> A lot, a lot. >> And how does that impact your role in IT? >> A lot, actually National Bank for Abu Dhabi security is one of our label, one of the safest banks, one of the 50 safest banks, this is one of our label. And we take security, we are the first customer in the Middle East to implement the RSA talking for login for online banking. This was eight years back. >> What about, I got to ask you, I follow a little bit of the banking executives and see what they're talking about and blockchain comes up a lot. This notion of trust between two parties and so forth. Do you see that as a mega-trend, is it something that's sort of an interesting trend? I mean what're your thoughts on that? >> It is the future, this is the future. >> It's the future? >> This is the future of the money. Money will be part to part with encryption and with the mechanism to get keys. And this is, people who don't want, this is where the money is moving in. >> So what happens to-- >> Actually what blocks this from happening on a wider scale is it's still, the regulation is not enough around this area, there is not enough regulation, so good money can mix with bad money and to be untraceables, he says this is one of the things. So people are starting putting a certain regulation across to start regulating, it will take time but it will be there. >> So blockchain is interesting, banking executives saying it's the future because everybody says oh blockchain's going to disrupt the banks but what I'm hearing from you is it's both, maybe it's a threat but it's also an opportunity because right now it's all done in secret, and so there needs to be. >> It's not in secret, it's done in Swift, the only platform for exchanging money worldwide is Swift. And sooner or later this will be changing, though some people are blockers, some people are, but it will be changing and the banks will realize this, adapt to blockchain technology. There's some legal services for blockchain and sooner you'll find central banks across the whole worldwide are starting regulating the blockchain. >> Yeah, okay it's very interesting because everybody says it's such a disruptive force which it is. >> It is a disruptive force. >> But it's also an, as you say, an opportunity. Where does Veeam fit with your availability strategy? >> It is in the center of our availability strategy, >> Really? >> Veeam is, I'm running my data centers and 90% of my data center is virtualized. So Veeam is running the whole virtual world in my data center, the 10% is running on traditional backup solution, which is, still we can't get rid of because you know in banking you have a critical system that measures 20 to 80 so you have running 80% of your system in virtual but actually the core system is the 20% so this is still not virtualized. Your core banking legacy application and some other stuff, so you have still to take the back up in traditional ways. And we mix both together, we mix back up data backing up the information, back up the virtual environment, this is very important for me. >> Dave: And you have a service catalog essentially? >> Mahmoud: We have service catalog for restore. >> For your data protection services right? >> For data protection of course. >> And what, like bronze, silver, gold levels or is it more granular than that or less granular? >> We have more, actually, in discussion with business we all were trying to meet the business requirements. So the most important for business in the banking section is the availability of the communication, mailboxes, availability of the critical detail in the databases. This is the things that we are taking care of. And Veeam has done a lot of development over the time, I'm first customer for Veeam from the Middle East by the way. >> Really? >> Yes. (laughter) >> Pioneer. (laughter) >> We are pioneer, and we've done it after trying them extensively in our environment and we decided that this is the best solution this time to go for virtual environment. It was extensive evaluation done across multiple vendor and we select them. >> And did that give you a competitive advantage? >> Of course, of course. >> How so? >> The ability to restore faster, the ability to do uptime faster, the ability to recover and ensure your back up is secure. This is something very valuable. >> Some of the other customers we've spoken to today have said that the announcements were meeting some of the wishlist that they have. Anything jump out at you with the announcements made so far? Things on your list that they-- >> Actually the continuous application is a very good feature. >> In the CDP, yeah. >> Yeah. It is one of the most important features we used as a traditional application of Vim backup. Last year, to move my data center from place to place, we were hosted somewhere and we moved the data center, the whole data center. It is good, but it was taking time, so continuous application will give you the availability of all those, just divert your backup somewhere and you'll be up and running. This is a very important feature. >> Anything that is on your wishlist that they haven't gotten to yet or things that you're looking for? >> I see, I see, Veeam should more personal devices, we are talking about IoT, internet of things, bring your own devices to work, so we should go more backing up the usual details on mobiles on at least the corporate one on the corporate bubble like Citrix, like whatever good solution, whatever solution is used. You can backing up design, backup solution for this, for end users this will be a huge thing. This will be, and part of the security of the organization cause you need all those to ensure if you add device of one employee you can restore this data back and ensure this data is restorable. >> This is the pain point for you today. >> It is. >> And it's going to get worse. I'll give you the last word, things have been exciting you at the show or conversations that you've had. >> No actually the keynote speech today was amazing, especially when you're understanding the direction of big companies like Cisco, VMWare, this is what's very valuable, very valuable. >> Excellent, well listen, thanks very much for coming we really appreciate it. >> Thank you. >> You're welcome. Alright, keep it right there everybody, Stu and I will be back rapid fire from VeeamOn 2017, be right back. (funky music) (tense music)

>> Voiceover: Live from Las Vegas, it's theCUBE, covering Discover 2016, Las Vegas. Brought to you by Hewlett Packard Enterprise. Now, here are your hosts, John Furrier and Dave Vellante. >> Okay, welcome back everyone. We are here live in Las Vegas for SiliconANGLE Media's theCUBE. It's our flagship program, we go out to the events to extract the signal from the noise, we're your exclusive coverage of HP Enterprise, Discover 2016, I'm John Furrier with my co-host, Dave Vellante, extracting the signals from the noise with two great guests, Dr. Tom Bradicich, VP and General Manager of the servers and IoT systems, and Eric Starkloff, the EVP of Global Sales and Marketing at National Instruments, welcome back to theCUBE. >> Thank you. >> John: Welcome for the first time Cube alumni, welcome to theCUBE. >> Thank you. >> So we are seeing a real interesting historic announcement from HP, because not only is there an IoT announcement this morning that you are the architect of, but the twist that you're taking with IoT, is very cutting edge, kind of like I just had Google IO, and at these big conferences they always have some sort of sexy demo, that's to kind of show the customers the future, like AI, or you know, Oculus Rift goggles as the future of their application, but you actually don't have something that's futuristic, it's reality, you have a new product, around IoT, at the Edge, Edgeline, the announcements are all online. Tom, but you guys did something different. And Eric's here for a reason, we'll get to that in a second, but the announcement represents a significant bet. That you're making, and HP's making, on the future of IoT. Please share the vision, and the importance of this event. >> Well thank you, and it's great to be back here with you guys. We've looked around and we could not find anything that existed today, if you will, to satisfy the needs of this industry and our customers. So we had to create not only a new product, but a new product category. A category of products that didn't exist before, and the new Edgeline1000, and the Edgeline4000 are the first entrance into this new product category. Now, what's a new product category? Well, whoever invented the first automobile, there was not a category of automobiles. When the first automobile was invented, it created a new product category called automobiles, and today everybody has a new entry into that as well. So we're creating a new product category, called converged IoT systems. Converged IoT systems are needed to deliver the real-time insights, real-time response, and advance the business outcomes, or the engineering outcomes, or the scientific outcomes, depending on the situation of our customers. They're needed to do that. Now when you have a name, converged, that means somewhat, a synonym is integration, what did we integrate? Now, I want to tell you the three major things we integrated, one of which comes from Eric, and the fine National Instruments company, that makes this technology that we actually put in, to the single box. And I can't wait to tell you more about it, but that's what we did, a new product category, not just two new products. >> So, you guys are bringing two industries together, again, that's not only just point technologies or platforms, in tooling, you're bringing disparate kind of players together. >> Yes. >> But it's not just a partnership, it's not like shaking hands and doing a strategic partnership, so there's real meat on the bone here. Eric, talk about one, the importance of this integration of two industries, basically, coming together, converged category if you will, or industry, and what specifically is in the box or in the technology. >> Yeah, I think you hit it exactly right. I mean, everyone talks about the convergence of OT, or operational technology, and IT. And we're actually doing it together. I represent the OT side, National Instruments is a global leader. >> John: OT, it means, just for the audience? >> Operational Technology, it's basically industrial equipment, measurement equipment, the thing that is connected to the real world. Taking data and controlling the thing that is in the internet of things, or the industrial internet of things as we play. And we've been doing internet of... >> And IT is Information Technologies, we know what that is, OT is... >> I figured that one you knew, OT is Operational Technology. We've been doing IoT before it was a buzzword. Doing measurement and control systems on industrial equipment. So when we say we're making it real, this Edgeline system actually incorporates in National Instruments technology, on an industry standard called PXI. And it is a measurement and control standard that's ubiquitous in the industry, and it's used to connect to the real world, to connect to sensors, actuators, to take in image data, and temperature data and all of those things, to instrument the world, and take in huge amounts of analog data, and then apply the compute power of an Edgeline system onto that application. >> We don't talk a lot about analog data in the IT world. >> Yeah. >> Why is analog data so important, I mean it's prevalent obviously in your world. Talk a little bit more about that. >> It's the largest source of data in the world, as Tom says it's the oldest as well. Analog, of course if you think about it, the analog world is literally infinite. And it's only limited by how many things we want to measure, and how fast we measure them. And the trend in technology is more measurement points and faster. Let me give you a couple of examples of the world we live in. Our customers have acquired over the years, approximately 22 exabytes of data. We don't deal with exabytes that often, I'll give an analogy. It's streaming high definition video, continuously, for a million years, produces 22 exabytes of data. Customers like CERN, that do the Large Hadron Collider, they're a customer of ours, they take huge amounts of analog data. Every time they do an experiment, it's the equivalent of 14 million images, photographs, that they take per second. They create 25 petabytes of data each year. The importance of this and the importance of Edgeline, and we'll get into this some, is that when you have that quantity of data, you need to push processing, and compute technology, towards the edge. For two main reasons. One, is the quantity of data, doesn't lend itself, or takes up too much bandwidth, to be streaming all of it back to central, to cloud, or centralized storage locations. The other one that's very, very important is latency. In the applications that we serve, you often need to make a decision in microseconds. And that means that the processing needs to be done, literally the speed of light is a limiting factor, the processing must be done on the edge, at the thing itself. >> So basically you need a data center at the edge. >> A great way to say it. >> A great way to say it. And this data, or big analog data as we love to call it, is things like particulates, motion, acceleration, voltage, light, sound, location, such as GPS, as well as many other things like vibration and moisture. That is the data that is pent up in things. In the internet of things. And Eric's company National Instruments, can extract that data, digitize it, make it ones and zeroes, and put it into the IT world where we can compute it and gain these insights and actions. So we really have a seminal moment here. We really have the OT industry represented by Eric, connecting with the IT industry, in the same box, literally in the same product in the box, not just a partnership as you pointed out. In fact it's quite a moment, I think we should have a photo op here, shaking hands, two industries coming together. >> So you talk about this new product category. What are the parameters of a new product category? You gave an example of an automobile, okay, but nobody had ever seen one before, but now you're bringing together sort of two worlds. What defines the parameters of a product category, such that it warrants a new category? >> Well, in general, never been done before, and accomplishes something that's not been done before, so that would be more general. But very specifically, this new product, EL1000 and EL4000, creates a new product category because this is an industry first. Never before have we taken data acquisition and capture technology from National Instruments, and data control technology from National Instruments, put that in the same box as deep compute. Deep x86 compute. What do I mean by deep? 64 xeon cores. As you said, a piece of the data center. But that's not all we converged. We took Enterprise Class systems management, something that HP has done very well for many, many years. We've taken the Hewlett Packard Enterprise iLo lights-out technology, converged that as well. In addition we put storage in there. 10s of terabytes of storage can be at the edge. So by this combination of things, that did exist before, the elements of course, by that combination of things, we've created this new product category. >> And is there a data store out there as well? A database? >> Oh yes, now since we have, this is the profundity of what I said, lies in the fact that because we have so many cores, so close to the acquisition of the data, from National Instruments, we can run virtually any application that runs on an x86 server. So, and I'm not exaggerating, thousands. Thousands of databases. Machine learning. Manageability, insight, visualization of data. Data capture tools, that all run on servers and workstations, now run at the edge. Again, that's never been done before, in the sense that at the edge today, are very weak processing. Very weak, and you can't just run an unmodified app, at that level. >> And in terms of the value chain, National Instruments is a supplier to this new product category? Is that the right way to think about it? >> An ingredient, a solution ingredient but just like we are, number one, but we are both reselling the product together. >> Dave: Okay. >> So we've jointly, collaboratively, developed this together. >> So it's engineers and engineers getting together, building the product. >> Exactly. His engineers, mine, we worked extremely close, and produced this beauty. >> We had a conversation yesterday, argument about the iPhone, I was saying hey, this was a game-changing category, if you will, because it was a computer that had software that could make phone calls. Versus the other guys, who had a phone, that could do text messages and do email. With a browser. >> Tom: With that converged product. >> So this would be similar, if I may, and you can correct me if I'm wrong, I want you to correct me and clarify, what you're saying is, you guys essentially looked at the edge differently, saying let's build the data center, at the edge, in theory or in concept here, in a little concept, but in theory, the power of a data center, that happens to do edge stuff. >> Tom: That's right. >> Is that accurate? >> I think it's very accurate. Let me make a point and let you respond. >> Okay. >> Neapolitan ice cream has three flavors. Chocolate, vanilla, strawberry, all in one box. That's what we did with this Edgeline. What's the value of that? Well, you can carry it, you can store it, you can serve it more conveniently, with everything together. You could have separate boxes, of chocolate, vanilla, and strawberry, that existed, right, but coming together, that convergence is key. We did that with deep compute, with data capture and control, and then systems management and Enterprise class device and systems management. And I'd like to explain why this is a product. Why would you use this product, you know, as well. Before I continue though, I want to get to the seven reasons why you would use this. And we'll go fast. But seven reasons why. But would you like to add anything about the definition of the conversion? >> Yeah, I was going to just give a little perspective, from an OT and an industrial OT kind of perspective. This world has generally lived in a silo away from IT. >> Mm-hmm. >> It's been proprietary networking standards, not been connected to the rest of the enterprise. That's the huge opportunity when we talk about the IoT, or the industrial IT, is connecting that to the rest of the enterprise. Let me give you an example. One of our customers is Duke Energy. They've implemented an online monitoring system for all of their power generation plants. They have 2,000 of our devices called CompactRIO, that connect to 30,000 sensors across all of their generation plants, getting real-time monitoring, predictive analytics, predictive failure, and it needs to have processing close to the edge, that latency issue I mentioned? They need to basically be able to do deep processing and potentially shut down a machine. Immediately if it's an a condition that warrants so. The importance here is that as those things are brought online, into IT infrastructure, the importance of deep compute, and the importance of the security and the capability that HPE has, becomes critical to our customers in the industrial internet of things. >> Well, I want to push back and just kind of play devil's advocate, and kind of poke holes in your thesis, if I can. >> Eric: Sure thing. >> So you got the probes and all the sensors and all the analog stuff that's been going on for you know, years and years, powering and instrumentation. You've got the box. So okay, I'm a customer. I have other stuff I might put in there, so I don't want to just rely on just your two stuff. Your technologies. So how do you deal with the corner case of I might have my own different devices, it's connected through IT, is that just a requirement on your end, or is that... How do you deal with the multi-vendor thing? >> It has to be an open standard. And there's two elements of open standard in this product, I'll let Tom come in on one, but one of them is, the actual IO standard, that connects to the physical world, we said it's something called PXI. National Instruments is a major vendor within this PXI market, but it is an open standard, there are 70 different vendors, thousands of products, so that part of it in connecting to the physical world, is built on an open standard, and the rest of the platform is as well. >> Indeed. Can I go back to your metaphor of the smartphone that you held up? There are times even today, but it's getting less and less, that people still carry around a camera. Or a second phone. Or a music player. Or the Beats headphones, et cetera, right? There's still time for that. So to answer your question, it's not a replacement for everything. But very frankly, the vision is over time, just like the smartphone, and the app store, more and more will get converged into this platform. So it's an introduction of a platform, we've done the inaugural convergence of the aforementioned data capture, high compute, management, storage, and we'll continue to add more and more, again, just like the smartphone analogy. And there will still be peripheral solutions around, to address your point. >> But your multi-vendor strategy if I get this right, doesn't prevent you, doesn't foreclose the customer's benefits in any way, so they connect through IT, they're connected into the box and benefits. You changed, they're just not converged inside the box. >> At this point. But I'm getting calls regularly, and you may too, Eric, of other vendors saying, I want in. I would like to relate that conceptually to the app store. Third party apps are being produced all the time that go onto this platform. And it's pretty exciting. >> And before you get to your seven killer attributes, what's the business model? So you guys have jointly engineered this product, you're jointly selling it through your channels, >> Eric: Yes. >> If you have a large customer like GE for example, who just sort of made the public commitment to HPE infrastructure. How will you guys "split the booty," so to speak? (laughter) >> Well we are actually, as Tom said we are doing reselling, we'll be reselling this through our channel, but I think one of the key things is bringing together our mutual expertise. Because when we talk about convergence of OT and IT, it's also bringing together the engineering expertise of our two companies. We really understand acquiring data from the real world, controlling industrial systems. HPE is the world leader in IT technology. And so, we'll be working together and mutually with customers to bring those two perspectives together, and we see huge opportunity in that. >> Yeah, okay so it's engineering. You guys are primarily a channel company anyway, so. >> Actually, I can make it frankly real simple, knowing that if we go back to the Neapolitan ice cream, and we reference National Instruments as chocolate, they have all the contact with the chocolate vendor, the chocolate customers if you will. We have all the vanilla. So we can go in and then pull each other that way, and then go in and pull this way, right? So that's one way as this market develops. And that's going to very powerful because indeed, the more we talk about when it used to be separated, before today, the more we're expressing that also separate customers. That the other guy does not know. And that's the key here in this relationship. >> So talk about the trend we're hearing here at the show, I mean it's been around in IT for a long time. But more now with the agility, the DevOps and cloud and everything. End to end management. Because that seems to be the table stakes. Do you address any of that in the announcement, is it part, does it fit right in? >> Absolutely, because, when we take, and we shift left, this is one of our monikers, we shift left. The data center and the cloud is on the right, and we're shifting left the data center class capabilities, out to the edge. That's why we call it shift left. And we meet, our partner National Instruments is already there, and an expert and a leader. As we shift left, we're also shifting with it, the manageability capabilities and the software that runs the management. Whether it be infrastructure, I mean I can do virtualization at the edge now, with a very popular virtualization package, I can do remote desktops like the Citrix company, the VMware company, these technologies and databases that come from our own Vertica database, that come from PTC, a great partner, with again, operations technology. Things that were running already in the data center now, get to run there. >> So you bring the benefit to the IT guy, out to the edge, to management, and Eric, you get the benefit of connecting into IT, to bring that data benefits into the business processes. >> Exactly. And as the industrial internet of things scales to billions of machines that have monitoring, and online monitoring capability, that's critical. Right, it has to be manageable. You have to be able to have these IT capabilities in order to manage such a diverse set of assets. >> Well, the big data group can basically validate that, and the whole big data thesis is, moving data where it needs to be, and having data about physical analog stuff, assets, can come in and surface more insight. >> Exactly. The biggest data of all. >> And vice versa. >> Yup. >> All right, we've got to get to the significant seven, we only have a few minutes left. >> All right. Oh yeah. >> Hit us. >> Yeah, yeah. And we're cliffhanging here on that one. But let me go through them real quick. So the question is, why wouldn't I just, you know, rudimentary collect the data, do some rudimentary analytics, send it all up to the cloud. In fact you hear that today a lot, pop-up. Censored cloud, censored cloud. Who doesn't have a cloud today? Every time you turn around, somebody's got a cloud, please send me all your data. We do that, and we do that well. We have Helion, we have the Microsoft Azure IoT cloud, we do that well. But my point is, there's a world out there. And it can be as high as 40 to 50 percent of the market, IDC is quoted as suggesting 40 percent of the data collected at the edge, by for example National Instruments, will be processed at the edge. Not sent, necessarily back to the data center or cloud, okay. With that background, there are seven reasons to not send all the data, back to the cloud. That doesn't mean you can't or you shouldn't, it just means you don't have to. There are seven reasons to compute at the edge. With an Edgeline system. Ready? >> Dave: Ready. >> We're going to go fast. And there'll be a test on this, so. >> I'm writing it down. >> Number one is latency, Eric already talked about that. How fast do you want your turnaround time? How fast would you like to know your asset's going to catch on fire? How fast would you like to know when the future autonomous car, that there's a little girl playing in the road, as opposed to a plastic bag being blown against the road, and are you going to rely on the latency of going all the way to the cloud and back, which by the way may be dropped, it's not only slow, but you ever try to make a phone call recently, and it not work, right? So you get that point. So that's latency one. You need to time to incite, time to response. Number one of seven, I'll go real quick. Number two of seven is bandwidth. If you're going to send all this big analog data, the oldest, the fastest, and the biggest of all big data, all back, you need tremendous bandwidth. And sometimes it doesn't exist, or, as some of our mutual customers tell us, it exists but I don't want to use it all for edge data coming back. That's two of seven. Three of seven is cost. If you're going to use the bandwidth, you've got to pay for it. Even if you have money to pay for it, you might not want to, so again that's three, let's go to four. (coughs) Excuse me. Number four of seven is threats. If you're going to send all the data across sites, you have threats. It doesn't mean we can't handle the threats, in fact we have the best security in the industry, with our Aruba security, ClearPass, we have ArcSight, we have Volt. We have several things. But the point is, again, it just exposes it to more threats. I've had customers say, we don't want it exposed. Anyway, that's four. Let's move on to five, is duplication. If you're going to collect all the data, and then send it all back, you're going to duplicate at the edge, you're going to duplicate not all things, but some things, both. All right, so duplication. And here we're coming up to number six. Number six is corruption. Not hostile corruption, but just package dropped. Data gets corrupt. The longer you have it in motion, e.g. back to the cloud, right, the longer it is as well. So you have corruption, you can avoid. And number three, I'm sorry, number seven, here we go with number seven. Not to send all the data back, is what we call policies and compliance, geo-fencing, I've had a customer say, I am not allowed to send all the data to these data centers or to my data scientists, because I can't leave country borders. I can't go over the ocean, as well. Now again, all these seven, create a market for us, so we can solve these seven, or at least significantly ameliorate the issues by computing at the edge with the Edgeline systems. >> Great. Eric, I want to get your final thoughts here, and as we wind down the segment. You're from the ops side, ops technologies, this is your world, it's not new to you, this edge stuff, it's been there, been there, done that, it is IoT for you, right? So you've seen the evolution of your industry. For the folks that are in IT, that HP is going to be approaching with this new category, and this new shift left, what does it mean? Share your color behind, and reasoning and reality check, on the viability. >> Sure. >> And relevance. >> Yeah, I think that there are some significant things that are driving this change. The rise of software capability, connecting these previously siloed, unconnected assets to the rest of the world, is a fundamental shift. And the cost point of acquisition technology has come down the point where we literally have a better, more compelling economic case to be made, for the online monitoring of more and more machine-type data. That example I gave of Duke Energy? Ten years ago they evaluated online monitoring, and it wasn't economical, to implement that type of a system. Today it is, and it's actually very, very compelling to their business, in terms of scheduled downtime, maintenance cost, it's a compelling value proposition. And the final one is as we deliver more analytics capability to the edge, I believe that's going to create opportunity that we don't even really, completely envision yet. And this deep computing, that the Edgeline systems have, is going to enable us to do an analysis at the edge, that we've previously never done. And I think that's going to create whole new opportunities. >> So based on your expert opinion, talk to the IT guys watching, viability, and ability to do this, what's the... Because some people are a little nervous, will the parachute open? I mean, it's a huge endeavor for an IT company to instrument the edge of their business, it's the cutting, bleeding edge, literally. What's the viability, the outcome, is it possible? >> It's here now. It is here now, I mean this announcement kind of codifies it in a new product category, but it's here now, and it's inevitable. >> Final word, your thoughts. >> Tom: I agree. >> Proud papa, you're like a proud papa now, you got your baby out there. >> It's great. But the more I tell you how wonderful the EL1000, EL4000 is, it's like my mother calling me handsome. Therefore I want to point the audience to Flowserve. F-L-O-W, S-E-R-V-E. They're one of our customers using Edgeline, and National Instruments equipment, so you can find that video online as well. They'll tell us about really the value here, and it's really powerful to hear from a customer. >> John: And availability is... >> Right now we have EL1000s and EL4000s in the hands of our customers, doing evaluations, at the end of the summer... >> John: Pre-announcement, not general availability. >> Right, general availability is not yet, but we'll have that at the end of the summer, and we can do limited availability as we call it, depending on the demand, and how we roll it out, so. >> How big the customer base is, in relevance to the... Now, is this the old boon shot box, just a quick final question. >> Tom: It is not, no. >> Really? >> We are leveraging some high-performance, low-power technology, that Intel has just announced, I'd like to shout out to that partner. They just announced and launched... Diane Bryant did her keynote to launch the new xeon, E3, low-power high-performance xeon, and it was streamed, her keynote, on the Edgeline compute engine. That's actually going into the Edgeline, that compute blade is going into the Edgeline. She streamed with it, we're pretty excited about that as well. >> Tom and Eric, thanks so much for sharing the big news, and of course congratulations, new category. >> Thank you. >> Let's see how this plays out, we'll be watching, got to get the draft picks in for this new sports league, we're calling it, like IoT, the edge, of course we're theCUBE, we're living at the edge, all the time, we're at the edge of HPE Discovery. Have one more day tomorrow, but again, three days of coverage. You're watching theCUBE, I'm John Furrier with Dave Vellante, we'll be right back. (electronic music)

(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

(upbeat music) >> Hello, welcome to the Cube's coverage of International Women's Day 2023. I'm John Furrier, host of the CUBE series of profiles around leaders in the tech industry sharing their stories, advice, best practices, what they're doing in their jobs their vision of the future, and more importantly, passing it on and encouraging more and more networking and telling the stories that matter. Our next guest is a great executive leader talking about how to lead in challenging times. Krista Satterthwaite, who is Senior Vice President and GM of Mainstream Compute. Krista great to see you're Cube alumni. We've had you on before talking about compute power. And by the way, congratulations on your BPT and Black Professional Tech Network 2023 Black Tech Exec of the Year Award. >> Thank you very much. Appreciate it. And thanks for having me. >> I knew I liked you the first time we were doing interviews together. You were so smart and so on top of it. Thanks for coming on. >> No problem. >> All kidding aside, let's get into it. You know, one of the things that's coming out on these interviews is leadership is being showcased and there's a network effect happening in the industry and you're starting to see people look and hear stories that they may or may not have heard before or news stories are coming out. So, one of the things that's interesting is that also in the backdrop of post pandemic, there's been a turn in the industry a little bit, there's a little bit of headwind in certain areas, some tailwinds in cloud and other areas. Compute, your area is doing very well. It could be challenging. And as a leader, has the conversation changed? And where are you at right now in the network of folks you're working with? What's the mood? >> Yeah, so actually I, things are much better. Obviously we had a chip shortage last year. Things are much, much better. But I learned a lot when it came to going through challenging times and leadership. And I think when we talk to customers, a lot of 'em are in challenging situations. Sometimes it's budget, sometimes it's attracting and retaining talent and sometimes it's just demands because, it's really exciting that technology is behind everything. But that means the demands on IT are bigger than ever before. So what I find when it comes to challenging times is that there's really three qualities that are game changers when it comes to leading and challenging times. And the first one is positivity. People have to feel like there's a light at the end of the tunnel to make sure that, their attitudes stay up, that they stay working really really hard and they look to the leader for that. The second one is communication. And I read somewhere that communication is leadership. And we had a great example from our CEO Antonio Neri when the pandemic hit and everything shut down. He had an all employee meeting every week for a month and we have tens of thousands of employees. And then even after that month, we had 'em very regularly. But he wanted to make sure that everybody heard from, him his thoughts had all the updates, knew how their peers were doing, how we were helping customers. And I really learned a lot from that in terms of communicating and communicating more during tough times. And then I would say the third one is making sure that they are informed and they feel empowered. So I would say a leader who is able to do that really, really stands out in a challenging time. >> So how do you get yourself together? Obviously you the chip shortage everyone knows in the industry and for the folks not in the tech industry, it was an economic potential disaster, because you don't get the chips you need. You guys make servers and technology, chips power everything. If you miss a shipment, it could cause a lot of backlash. So Cisco had an earnings impact. It has impact to the business. When do you have that code red moment where it's like, okay, we have to kind of put the pause and go into emergency mode. And how do you handle that? >> Well, you know, it is funny 'cause when it, when we have challenges, I come to learn that people can look at challenges and hard work as a burden or a mission and they behave totally different. If they see it as a burden, then they're doing the bare minimum and they're pointing fingers and they're complaining and they're probably not getting a whole lot done. If they see it as a mission, then all of a sudden they're going above and beyond. They're working really hard, they're really partnering. And if it affects customers for HPE, obviously we, HPE is a very customer centric company, so everyone pays attention and tries to pitch in. But when it comes to a mission, I started thinking, what are the real ingredients for a mission? And I think it's important. I think it's, people feel like they can make an impact. And then I think the third one is that the goal is clear, even if the path isn't, 'cause you may have to pivot a lot if it's a challenge. And so when it came to the chip shortage, it was a mission. We wanted to make sure that we could ship to customers as quickly as possible. And it was a mission. Everybody pulled together. I learned how much our team could pull off and pull together through that challenge. >> And the consequences can be quantified in economics. So it's like the burn the boats example, you got to burn the boats, you're stuck. You got to figure out a solution. How does that change the demands on people? Because this is, okay, there's a mission it they're not, it's not normal. What are some of those new demands that arise during those times and how do you manage that? How do you be a leader? >> Yeah, so it's funny, I was reading this statement from James White who used to be the CEO of Jamba Juice. And he was talking about how he got that job. He said, "I think it was one thing I said that really convinced them that I was the right person." And what he said was something like, "I will get more out of people than nine out of 10 leaders on the planet." He said, "Because I will look at their strengths and their capabilities and I will play to their passions." and their capabilities and I will play their passions. and getting the most out people in difficult times, it is all about how much you can get out of people for their own sake and for the company's sake. >> That's great feedback. And to people watching who are early in their careers, leading is getting the best out of your team, attitude. Some of the things you mentioned. What advice would you give folks that are starting to get into the workforce, that are starting to get into that leadership track or might have a trajectory or even might have an innate ability that they know they have and they want to pursue that dream? >> Yeah so. >> What advice would you give them? >> Yeah, what I would say, I say this all the time that, for the first half of my career I was very job conscious, but I wasn't very career conscious. So I'd get in a role and I'd stay in that role for long periods of time and I'd do a good job, but I wasn't really very career conscious. And what I would say is, everybody says how important risk taking is. Well, risk taking can be a little bit of a scary word, right? Or term. And the way I see it is give it a shot and see what happens. You're interested in something, give it a shot and see what happens. It's kind of a less intimidating way of looking at risk because even though I was job conscious, and not career conscious, one thing I did when people asked me to take something on, hey Krista, would you like to take on more responsibility here? The answer was always yes, yes, yes, yes. So I said yes because I said, hey I'll give it a shot and see what happens. And that helped me tremendously because I felt like I am giving it a try. And the more you do that, the the better it is. >> It's great. >> And actually the the less scary it is because you do that, a few times and it goes well. It's like a muscle that builds. >> It's funny, a woman executive was on the program. I said, the word balance comes up a lot. And she stopped and said, "Let's just talk about balance for a second." And then she went contrarian and said, "It's about not being unbalanced. It's about being, taking a chance and being a little bit off balance to put yourself outside your comfort zone to try new things." And then she also came up and followed and said, "If you do that alone, you increase your risk. But if you do it with people, a team that you trust and you're authentic and you're vulnerable and you're communicating, that is the chemistry." And that was a really good point. What's your reaction? 'Cause you were talking about authentic conversations good communications with Antonio. How does someone get, feel, find that team and do you agree with it? And what was your, how would you react to that? >> Yes, I agree with that. And when it comes to being authentic, that's the magic and when someone isn't, if someone's not really being themselves, it's really funny because you can feel it, you can sense it. There's kind of a wall between you and them. And over time people won't be able to put their finger on it, but they'll feel a distance from you. But when you're authentic and you share who you are, what you find is you find things in common with other people. 'Cause you're sharing more of who you are and it's like, oh, I do that too. Oh, I'm interested in that too. And build the bonds between people and the authenticity. And that's what people crave. They want people to be authentic and people can tell when you're authentic and when you're not. >> Is managing and leading through a crisis a born talent or can you learn it? >> Oh, definitely learned. I think that we're born knowing nothing and I once read people are nurtured into greatness and I think that's true. So yeah, definitely learned. >> What are some examples that can come out of a tough time as folks may look at a crisis and be shy away from it? How do they lean into it? What advice would you give folks? How do you handle it? I mean, everyone's got different personality. Okay, they get to a position but stepping through that door. >> Yeah, well, I do this presentation called, "10 things I Wish I Knew Earlier in my Career." And one of those things is about the growth mindset and the growth mindset. There's a book called "Mindset" by Carol Dweck and the growth mindset is all about learning and not always having to know everything, but really the winning is in the learning. And so if you have a growth mindset it makes you feel better about everything because you can't lose. You're winning because you're learning. So when I've learned that, I started looking at things much differently. And when it comes to going through tough times, what I find is you're exercising muscles that you didn't even know you had, which makes you stronger when the crisis is over, obviously. And I also feel like you become a lot a much more creative when you're in challenging times. You're forced to do things that you hadn't had to do before. And it also bonds the team. It's almost like going through bootcamp together. When you go through a challenge together it bonds you for life. >> I mean, you could have bonding, could be trauma bonding or success bonding. People love to be on the success side because that's positive and that's really the key mindset. You're always winning if you have that attitude. And learnings is also positive. So it's not, it's never a failure unless you make it. >> That's right, exactly. As long as you learn from it. And that's the name of the game. So, learning is the goal. >> So I have to ask you, on your job now, you have a really big responsibility HPE compute and big division. What's the current mindset that you have right now in your career, where you're at? What are some of the things on your mind that you think about? We had other, other seniors leaders say, hey, you know I got the software as my brain and the hardware's my body. I like to keep software and hardware working together. What is your current state of your career and how you looking at it, what's next and what's going on in your mind right now? >> Yeah, so for me, I really want to make sure that for my team we're nurturing the next generation of leadership and that we're helping with career development and career growth. And people feel like they can grow their careers here. Luckily at HPE, we have a lot of people stay at HPE a long time, and even people who leave HPE a lot of times they come back because the culture's fantastic. So I just want to make sure I'm contributing to that culture and I'm bringing up the next generation of leaders. >> What's next for you? What are you looking at from a career personal standpoint? >> You know, it's funny, I, I love what I'm doing right now. I'm actually on a joint venture board with H3C, which is HPE Joint Venture Company. And so I'm really enjoying that and exploring more board service opportunities. >> You have a focus of good growth mindset, challenging through, managing through tough times. How do you stay focused on that North star? How do you keep the reinforcement of the mission? How do you nurture the team to greatness? >> Yeah, so I think it's a lot of clarity, providing a lot of clarity about what's important right now. And it goes back to some of the communication that I mentioned earlier, making sure that everybody knows where the North Star is, so everybody's focused on the same thing, because I feel like with the, I always felt like throughout my career I was set up for success if I had the right information, the right guidance and the right goals. And I try to make sure that I do that with my team. >> What are some of the things that you could share as we wrap up here for the folks watching, as the networks increase, as the stories start to unfold more and more on digital like we're doing here, what do you hope people walk away with? What's working, what needs work, and what is some things that people aren't talking about that should be discussed publicly? >> Do you mean from a career standpoint or? >> For career? For growing into tech and into leadership positions. >> Okay. >> Big migration tech is now a wide field. I mean, when I grew up, broke into the eighties, it was computer science, software engineering, and three degrees in engineering, right? >> I see huge swath of AI coming. So many technical careers. There's a lot more women. >> Yeah. And that's what's so exciting about being in a technical career, technical company, is that everything's always changing. There's always opportunity to learn something new. And frankly, you know, every company is in the business of technology right now, because they want to closer to their customers. Typically, they're using technology to do that. Everyone's digitally transforming. And so what I would say is that there's so much opportunity, keep your mind open, explore what interests you and keep learning because it's changing all the time. >> You know I was talking with Sue, former HP, she's on a lot of boards. The balance at the board level still needs a lot of work and the leaderships are getting better, but the board at the seats at the table needs work. Where do you see that transition for you in the future? Is that something on your mind? Maybe a board seat? You mentioned you're on a board with HPE, but maybe sitting on some other boards? Any, any? >> Yes, actually, actually, we actually have a program here at HPE called the Board Ready Now program that I'm a part of. And so HPE is very supportive of me exploring an independent board seat. And so they have some education and programming around that. And I know Sue well, she's awesome. And so yes, I'm looking into those opportunities right now. >> She advises do one no more than two. The day job. >> Yeah, I would only be doing one current job that I have. >> Well, kris, it was great to chat with you about these topics and leadership and challenging times. Great masterclass, great advice. As SVP and GM of mainstream compute for HPE, what's going on in your job these days? What's the most exciting thing happening? Share some of your work situations. >> Sure, so the most exciting thing happening right now is HPE Gen 11, which we just announced and started shipping, brings tremendous performance benefit, has an intuitive operating experience, a trusted security by design, and it's optimized to run workloads so much faster. So if anybody is interested, they should go check it out on hpe.com. >> And of course the CUBE will be at HPE Discover. We'll see you there. Any final wisdom you'd like to share as we wrap up the last minute here? >> Yeah, so I think the last thing I'll say is that when it comes to setting your sights, I think, expecting it, good things to happen usually happens when you believe you deserve it. So what happens is you believe you deserve it, then you expect it and you get it. And so sometimes that's about making sure you raise your thermostat to expect more. And I always talk about you don't have to raise it all up at once. You could do that incrementally and other people can set your thermostat too when they say, hey, you should be, you should get a level this high or that high, but raise your thermostat because what you expect is what you get. >> Krista, thank you so much for contributing to this program. We're going to do it quarterly. We're going to do getting more stories out there, so we'll have you back and if you know anyone with good stories, send them our way. And congratulations on your BPTN Tech Executive of the Year award for 2023. Congratulations, great prize there and great recognition for your hard work. >> Thank you so much, John, I appreciate it. >> Okay, this is the Cube's coverage of National Woodman's Day. I'm John Furrier, stories from the front lines, management ranks, developers, all there, global coverage of international events with theCUBE. Thanks for watching. (soft music)

(upbeat music) >> Hi, everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. As part of International Women's Day, we're featuring some of the leading women in business technology from developer to all types of titles and to the executive level. And one topic that's really important is called Getting a Seat at the Table, board makeup, having representation at corporate boards, private and public companies. It's been a big push. And former technology operating executive and corporate board member, she's a board machine Sue Barsamian, formerly with HPE, Hewlett Packard. Sue, great to see you. CUBE alumni, distinguished CUBE alumni. Thank you for coming on. >> Yes, I'm very proud of my CUBE alumni title. >> I'm sure it opens a lot of doors for you. (Sue laughing) We're psyched to have you on. This is a really important topic, and I want to get into the whole, as women advance up, and they're sitting on the boards, they can implement policy and there's governance. Obviously public companies have very strict oversight, and not strict, but like formal. Private boards have to operate, be nimble. They don't have to share all their results. But still, boards play an important role in the success of scaled up companies. So super important, that representation there is key. >> Yes. >> I want to get into that, but first, before we get started, how did you get into tech? How did it all start for you? >> Yeah, long time ago, I was an electrical engineering major. Came out in 1981 when, you know, opportunities for engineering, if you were kind, I went to Kansas State as an undergrad, and basically in those days you went to Texas and did semiconductors. You went to Atlanta and did communication satellites. You went to Boston or you went to Silicon Valley. And for me, that wasn't too hard a choice. I ended up going west and really, I guess what, embarked on a 40 year career in Silicon Valley and absolutely loved it. Largely software, but some time on the hardware side. Started out in networking, but largely software. And then, you know, four years ago transitioned to my next chapter, which is the corporate board director. And again, focused on technology software and cybersecurity boards. >> For the folks watching, we'll cut through another segment we can probably do about your operating career, but you rose through the ranks and became a senior operating executive at the biggest companies in the world. Hewlett Packard Enterprise, Hewlett Packard Enterprise and others. Very great career, okay. And so now you're kind of like, put that on pause, and you're moving on to the next chapter, which is being a board director. What inspired you to be a board director for multiple public companies and multiple private companies? Well, how many companies are you on? But what's the inspiration? What's the inspiration? First tell me how many board ships you're on, board seats you're on, and then what inspired you to become a board director? >> Yeah, so I'm on three public, and you are limited in terms of the number of publics that you can do to four. So I'm on three public, and I'm on four private from a tech perspective. And those range from, you know, a $4 billion in revenue public company down to a 35 person private company. So I've got the whole range. >> So you're like freelancing, I mean, what is it like? It's a full-time job, obviously. It's a lot of work involved. >> Yeah, yeah, it's. >> John: Why are you doing it? >> Well, you know, so I retired from being an operating executive after 37 years. And, but I loved, I mean, it's tough, right? It's tough these days, particularly with all the pressures out there in the market, not to mention the pandemic, et cetera. But I loved it. I loved working. I loved having a career, and I was ready to back off on, I would say the stresses of quarterly results and the stresses of international travel. You have so much of it. But I wasn't ready to back off from being involved and engaged and continuing to learn new things. I think this is why you come to tech, and for me, why I went to the valley to begin with was really that energy and that excitement, and it's like it's constantly reinventing itself. And I felt like that wasn't over for me. And I thought because I hadn't done boards before I retired from operating roles, I thought, you know, that would fill the bill. And it's honestly, it has exceeded expectations. >> In a good way. You feel good about where you're at and. >> Yeah. >> What you went in, what was the expectation going in and what surprised you? And were there people along the way that kind of gave you some pointers or don't do this, stay away from this. Take us through your experiences. >> Yeah, honestly, there is an amazing network of technology board directors, you know, in the US and specifically in the Valley. And we are all incredibly supportive. We have groups where we get together as board directors, and we talk about topics, and we share best practices and stories, and so I underestimated that, right? I thought I was going to, I thought I was going to enter this chapter where I would be largely giving back after 37 years. You've learned a little bit, right? What I underestimated was just the power of continuing to learn and being surrounded by so many amazing people. When, you know, when you do, you know, multiple boards, your learnings are just multiplied, right? Because you see not just one model, but you see many models. You see not just one problem, but many problems. Not just one opportunity, but many opportunities. And I underestimated how great that would be for me from a learning perspective and then your ability to share from one board to the other board because all of my boards are companies who are also quite close to each other, the executives collaborate. So that has turned out to be really exciting for me. >> So you had the stressful job. You rose to the top of the ranks, quarterly shot clock earnings, and it's hard charging. It's like, it's like, you know, being an athlete, as we say tech athlete. You're a tech athlete. Now you're taking that to the next level, which is now you're juggling multiple operational kind of things, but not with super pressure. But there's still a lot of responsibility. I know there's one board, you got compensation committee, I mean there's work involved. It's not like you're clipping coupons and having pizza. >> Yeah, no, it's real work. Believe me, it's real work. But I don't know how long it took me to not, to stop waking up and looking at my phone and thinking somebody was going to be dropping their forecast, right? Just that pressure of the number, and as a board member, obviously you are there to support and help guide the company and you feel, you know, you feel the pressure and the responsibility of what that role entails, but it's not the same as the frontline pressure every quarter. It's different. And so I did the first type. I loved it, you know. I'm loving this second type. >> You know, the retirement, it's always a cliche these days, but it's not really like what people think it is. It's not like getting a boat, going fishing or whatever. It's doing whatever you want to do, that's what retirement is. And you've chose to stay active. Your brain's being tested, and you're working it, having fun without all the stress. But it's enough, it's like going the gym. You're not hardcore workout, but you're working out with the brain. >> Yeah, no, for sure. It's just a different, it's just a different model. But the, you know, the level of conversations, the level of decisions, all of that is quite high. Which again, I like, yeah. >> Again, you really can't talk about some of the fun questions I want to ask, like what's the valuations like? How's the market, your headwinds? Is there tailwinds? >> Yes, yes, yes. It's an amazing, it's an amazing market right now with, as you know, counter indicators everywhere, right? Something's up, something's down, you know. Consumer spending's up, therefore interest rates go up and, you know, employment's down. And so or unemployment's down. And so it's hard. Actually, I really empathize with, you know, the, and have a great deal of respect for the CEOs and leadership teams of my board companies because, you know, I kind of retired from operating role, and then everybody else had to deal with running a company during a pandemic and then running a company through the great resignation, and then running a company through a downturn. You know, those are all tough things, and I have a ton of respect for any operating executive who's navigating through this and leading a company right now. >> I'd love to get your take on the board conversations at the end if we have more time, what the mood is, but I want to ask you about one more thing real quick before we go to the next topic is you're a retired operating executive. You have multiple boards, so you've got your hands full. I noticed there's a lot of amazing leaders, other female tech athletes joining boards, but they also have full-time jobs. >> Yeah. >> And so what's your advice? Cause I know there's a lot of networking, a lot of sharing going on. There's kind of a balance between how much you can contribute on the board versus doing the day job, but there's a real need for more women on boards, so yet there's a lot going on boards. What's the current state of the union if you will, state of the market relative to people in their careers and the stresses? >> Yeah. >> Cause you left one and jumped in all in there. >> Yeah. >> Some can't do that. They can't be on five boards, but they're on a few. What's the? >> Well, and you know, and if you're an operating executive, you wouldn't be on five boards, right? You would be on one or two. And so I spend a lot of time now bringing along the next wave of women and helping them both in their career but also to get a seat at the table on a board. And I'm very vocal about telling people not to do it the way I do it. There's no reason for it to be sequential. You can, you know, I thought I was so busy and was traveling all the time, and yes, all of that was true, but, and maybe I should say, you know, you can still fit in a board. And so, and what I see now is that your learnings are so exponential with outside perspective that I believe I would've been an even better operating executive had I done it earlier. I know I would've been an even better operating executive had I done it earlier. And so my advice is don't do it the way I did it. You know, it's worked out fine for me, but hindsight's 2020, I would. >> If you can go back and do a mulligan or a redo, what would you do? >> Yeah, I would get on a board earlier, full stop, yeah. >> Board, singular, plural? >> Well, I really, I don't think as an operating executive you can do, you could do one, maybe two. I wouldn't go beyond that, and I think that's fine. >> Yeah, totally makes sense. Okay, I got to ask you about your career. I know technical, you came in at that time in the market, I remember when I broke into the business, very male dominated, and then now it's much better. When you went through the ranks as a technical person, I know you had some blockers and definitely some, probably some people like, well, you know. We've seen that. How did you handle that? What were some of the key pivot points in your journey? And we've had a lot of women tell their stories here on theCUBE, candidly, like, hey, I was going to tell that professor, I'm going to sit in the front row. I'm going to, I'm getting two degrees, you know, robotics and aerospace. So, but they were challenged, even with the aspiration to do tech. I'm not saying that was something that you had, but like have you had experience like that, that you overcome? What were those key points and how did you handle them and how does that help people today? >> Yeah, you know, I have to say, you know, and not discounting that obviously this has been a journey for women, and there are a lot of things to overcome both in the workforce and also just balancing life honestly. And they're all real. There's also a story of incredible support, and you know, I'm the type of person where if somebody blocked me or didn't like me, I tended to just, you know, think it was me and like work harder and get around them, and I'm sure that some of that was potentially gender related. I didn't interpret it that way at the time. And I was lucky to have amazing mentors, many, many, many of whom were men, you know, because they were in the positions of power, and they made a huge difference on my career, huge. And I also had amazing female mentors, Meg Whitman, Ann Livermore at HPE, who you know well. So I had both, but you know, when I look back on the people who made a difference, there are as many men on the list as there are women. >> Yeah, and that's a learning there. Create those coalitions, not just one or the other. >> Yeah, yeah, yeah, absolutely. >> Well, I got to ask you about the, well, you brought up the pandemic. This has come up on some interviews this year, a little bit last year on the International Women's Day, but this year it's resonating, and I would never ask in an interview. I saw an interview once where a host asked a woman, how do you balance it all? And I was just like, no one asked men that. And so it's like, but with remote work, it's come up now the word empathy around people knowing each other's personal situation. In other words, when remote work happened, everybody went home. So we all got a glimpse of the backdrop. You got, you can see what their personal life was on Facebook. We were just commenting before we came on camera about that. So remote work really kind of opened up this personal side of everybody, men and women. >> Yeah. >> So I think this brings this new empathy kind of vibe or authentic self people call it. Is remote work an opportunity or a threat for advancement of women in tech? >> It's a much debated topic. I look at it as an opportunity for many of the reasons that you just said. First of all, let me say that when I was an operating executive and would try to create an environment on my team that was family supportive, I would do that equally for young or, you know, early to mid-career women as I did for early to mid-career men. And the reason is I needed those men, you know, chances are they had a working spouse at home, right? I needed them to be able to share the load. It's just as important to the women that companies give, you know, the partner, male or female, the partner support and the ability to share the love, right? So to me it's not just a woman thing. It's women and men, and I always tried to create the environment where it was okay to go to your soccer game. I knew you would be online later in the evening when the kids were in bed, and that was fine. And I think the pandemic has democratized that and made that, you know, made that kind of an everyday occurrence. >> Yeah the baby walks in. They're in the zoom call. The dog comes in. The leaf blower going on the outside the window. I've seen it all on theCUBE. >> Yeah, and people don't try to pretend anymore that like, you know, the house is clean, the dog's behaved, you know, I mean it's just, it's just real, and it's authentic, and I think that's healthy. >> Yeah. >> I do, you know, I also love, I also love the office, and you know, I've got a 31 year old and a soon to be 27 year old daughter, two daughters. And you know, they love going into the office, and I think about when I was their age, how just charged up I would get from being in the office. I also see how great it is for them to have a couple of days a week at home because you can get a few things done in between Zoom calls that you don't have to end up piling onto the weekend, and, you know, so I think it's a really healthy, I think it's a really healthy mix now. Most tech companies are not mandating five days in. Most tech companies are at two to three days in. I think that's a, I think that's a really good combination. >> It's interesting how people are changing their culture to get together more as groups and even events. I mean, while I got you, I might as well ask you, what's the board conversations around, you know, the old conferences? You know, before the pandemic, every company had like a user conference. Right, now it's like, well, do we really need to have that? Maybe we do smaller, and we do digital. Have you seen how companies are handling the in-person? Because there's where the relationships are really formed face-to-face, but not everyone's going to be going. But now certain it's clearly back to face-to-face. We're seeing that with theCUBE as you know. >> Yeah, yeah. >> But the numbers aren't coming back, and the numbers aren't that high, but the stakeholders. >> Yeah. >> And the numbers are actually higher if you count digital. >> Yeah, absolutely. But you know, also on digital there's fatigue from 100% digital, right? It's a hybrid. People don't want to be 100% digital anymore, but they also don't want to go back to the days when everybody got on a plane for every meeting, every call, every sales call. You know, I'm seeing a mix on user conferences. I would say two-thirds of my companies are back, but not at the expense level that they were on user conferences. We spend a lot of time getting updates on, cause nobody has put, interestingly enough, nobody has put T&E, travel and expense back to pre-pandemic levels. Nobody, so everybody's pulled back on number of trips. You know, marketing events are being very scrutinized, but I think very effective. We're doing a lot of, and, you know, these were part of the old model as well, like some things, some things just recycle, but you know, there's a lot of CIO and customer round tables in regional cities. You know, those are quite effective right now because people want some face-to-face, but they don't necessarily want to get on a plane and go to Las Vegas in order to do it. I mean, some of them are, you know, there are a lot of things back in Las Vegas. >> And think about the meetings that when you were an operating executive. You got to go to the sales kickoff, you got to go to this, go to that. There were mandatory face-to-faces that you had to go to, but there was a lot of travel that you probably could have done on Zoom. >> Oh, a lot, I mean. >> And then the productivity to the family impact too. Again, think about again, we're talking about the family and people's personal lives, right? So, you know, got to meet a customer. All right. Salesperson wants you to get in front of a customer, got to fly to New York, take a red eye, come on back. Like, I mean, that's gone. >> Yeah, and oh, by the way, the customer doesn't necessarily want to be in the office that day, so, you know, they may or may not be happy about that. So again, it's and not or, right? It's a mix. And I think it's great to see people back to some face-to-face. It's great to see marketing and events back to some face-to-face. It's also great to see that it hasn't gone back to the level it was. I think that's a really healthy dynamic. >> Well, I'll tell you that from our experience while we're on the topic, we'll move back to the International Women's Day is that the productivity of digital, this program we're doing is going to be streamed. We couldn't do this face-to-face because we had to have everyone fly to an event. We're going to do hundreds of stories that we couldn't have done. We're doing it remote. Because it's better to get the content than not have it. I mean it's offline, so, but it's not about getting people to the event and watch the screen for seven hours. It's pick your interview, and then engage. >> Yeah. >> So it's self-service. So we're seeing a lot, the new user experience kind of direct to consumer, and so I think there will be an, I think there's going to be a digital first class citizen with events, so that that matches up with the kind of experience, but the offline version. Face-to-face optimized for relationships, and that's where the recruiting gets done. That's where, you know, people can build these relationships with each other. >> Yeah, and it can be asynchronous. I think that's a real value proposition. It's a great point. >> Okay, I want to get, I want to get into the technology side of the education and re-skilling and those things. I remember in the 80s, computer science was software engineering. You learned like nine languages. You took some double E courses, one or two, and all the other kind of gut classes in school. Engineering, you had the four class disciplines and some offshoots of specialization. Now it's incredible the diversity of tracks in all engineering programs and computer science and outside of those departments. >> Yeah. >> Can you speak to the importance of STEM and the diversity in the technology industry and how this brings opportunity to lower the bar to get in and how people can stay in and grow and keep leveling up? >> Yeah, well look, we're constantly working on how to, how to help the incoming funnel. But then, you know, at a university level, I'm on the foundation board of Kansas State where I got my engineering degree. I was also Chairman of the National Action Council for Minorities in Engineering, which was all about diversity in STEM and how do you keep that pipeline going because honestly the US needs more tech resources than we have. And if you don't tap into the diversity of our entire workforce, we won't be able to fill that need. And so we focused a lot on both the funnel, right, that starts at the middle school level, particularly for girls, getting them in, you know, the situation of hands-on comfort level with coding, with robot building, you know, whatever gives them that confidence. And then keeping that going all the way into, you know, university program, and making sure that they don't attrit out, right? And so there's a number of initiatives, whether it's mentoring and support groups and financial aid to make sure that underrepresented minorities, women and other minorities, you know, get through the funnel and stay, you know, stay in. >> Got it. Now let me ask you, you said, I have two daughters. You have a family of girls too. Is there a vibe difference between the new generation and what's the trends that you're seeing in this next early wave? I mean, not maybe, I don't know how this is in middle school, but like as people start getting into their adult lives, college and beyond what's the current point of view, posture, makeup of the talent coming in? >> Yeah, yeah. >> Certain orientations, do you see any patterns? What's your observation? >> Yeah, it's interesting. So if I look at electrical engineering, my major, it's, and if I look at Kansas State, which spends a lot of time on this, and I think does a great job, but the diversity of that as a major has not changed dramatically since I was there in the early 80s. Where it has changed very significantly is computer science. There are many, many university and college programs around the country where, you know, it's 50/50 in computer science from a gender mix perspective, which is huge progress. Huge progress. And so, and to me that's, you know, I think CS is a fantastic degree for tech, regardless of what function you actually end up doing in these companies. I mean, I was an electrical engineer. I never did core electrical engineering work. I went right into sales and marketing and general management roles. So I think, I think a bunch of, you know, diverse CS graduates is a really, really good sign. And you know, we need to continue to push on that, but progress has been made. I think the, you know, it kind of goes back to the thing we were just talking about, which is the attrition of those, let's just talk about women, right? The attrition of those women once they got past early career and into mid-career then was a concern, right? And that goes back to, you know, just the inability to, you know, get it all done. And that I am hopeful is going to be better served now. >> Well, Sue, it's great to have you on. I know you're super busy. I appreciate you taking the time and contributing to our program on corporate board membership and some of your story and observations and opinions and analysis. Always great to have you and call you a contributor for theCUBE. You can jump on on one more board, be one of our board contributors for our analysts. (Sue laughing) >> I'm at capacity. (both laughing) >> Final, final word. What's the big seat at the table issue that's going well and areas that need to be improved? >> So I'll speak for my boards because they have made great progress in efficiency. You know, obviously with interest rates going up and the mix between growth and profitability changing in terms of what investors are looking for. Many, many companies have had to do a hard pivot from grow at all costs to healthy balance of growth and profit. And I'm very pleased with how my companies have made that pivot. And I think that is going to make much better companies as a result. I think diversity is something that has not been solved at the corporate level, and we need to keep working it. >> Awesome. Thank you for coming on theCUBE. CUBE alumni now contributor, on multiple boards, full-time job. Love the new challenge and chapter you're on, Sue. We'll be following, and we'll check in for more updates. And thank you for being a contributor on this program this year and this episode. We're going to be doing more of these quarterly, so we're going to move beyond once a year. >> That's great. (cross talking) It's always good to see you, John. >> Thank you. >> Thanks very much. >> Okay. >> Sue: Talk to you later. >> This is theCUBE coverage of IWD, International Women's Day 2023. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(light music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. Dave Vellante with Dave Nicholson. Lisa Martin's been here all week. John Furrier is in our Palo Alto studio, banging out all the news. Don't forget to check out siliconangle.com, thecube.net. This is day four, our last segment, winding down. MWC23, super excited to be here. Wayne Duso, friend of theCUBE, VP of engineering from products at AWS is here with Iyad Tarazi, who's the CEO of Federated Wireless. Gents, welcome. >> Good to be here. >> Nice to see you. >> I'm so stoked, Wayne, that we connected before the show. We texted, I'm like, "You're going to be there. I'm going to be there. You got to come on theCUBE." So thank you so much for making time, and thank you for bringing a customer partner, Federated Wireless. Everybody knows AWS. Iyad, tell us about Federated Wireless. >> We're a software and services company out of Arlington, Virginia, right outside of Washington, DC, and we're really focused on this new technology called Shared Spectrum and private wireless for 5G. Think of it as enterprises consuming 5G, the way they used to consume WiFi. >> Is that unrestricted spectrum, or? >> It is managed, organized, interference free, all through cloud platforms. That's how we got to know AWS. We went and got maybe about 300 products from AWS to make it work. Quite sophisticated, highly available, and pristine spectrum worth billions of dollars, but available for people like you and I, that want to build enterprises, that want to make things work. Also carriers, cable companies everybody else that needs it. It's really a new revolution for everyone. >> And that's how you, it got introduced to AWS. Was that through public sector, or just the coincidence that you're in DC >> No, I, well, yes. The center of gravity in the world for spectrum is literally Arlington. You have the DOD spectrum people, you have spectrum people from National Science Foundation, DARPA, and then you have commercial sector, and you have the FCC just an Uber ride away. So we went and found the scientists that are doing all this work, four or five of them, Virginia Tech has an office there too, for spectrum research for the Navy. Come together, let's have a party and make a new model. >> So I asked this, I'm super excited to have you on theCUBE. I sat through the keynotes on Monday. I saw Satya Nadella was in there, Thomas Kurian there was no AWS. I'm like, where's AWS? AWS is everywhere. I mean, you guys are all over the show. I'm like, "Hey, where's the number one cloud?" So you guys have made a bunch of announcements at the show. Everybody's talking about the cloud. What's going on for you guys? >> So we are everywhere, and you know, we've been coming to this show for years. But this is really a year that we can demonstrate that what we've been doing for the IT enterprise, IT people for 17 years, we're now bringing for telcos, you know? For years, we've been, 17 years to be exact, we've been bringing the cloud value proposition, whether it's, you know, cost efficiencies or innovation or scale, reliability, security and so on, to these enterprise IT folks. Now we're doing the same thing for telcos. And so whether they want to build in region, in a local zone, metro area, on-prem with an outpost, at the edge with Snow Family, or with our IoT devices. And no matter where they want to start, if they start in the cloud and they want to move to the edge, or they start in the edge and they want to bring the cloud value proposition, like, we're demonstrating all of that is happening this week. And, and very much so, we're also demonstrating that we're bringing the same type of ecosystem that we've built for enterprise IT. We're bringing that type of ecosystem to the telco companies, with CSPs, with the ISP vendors. We've seen plenty of announcements this week. You know, so on and so forth. >> So what's different, is it, the names are different? Is it really that simple, that you're just basically taking the cloud model into telco, and saying, "Hey, why do all this undifferentiated heavy lifting when we can do it for you? Don't worry about all the plumbing." Is it really that simple? I mean, that straightforward. >> Well, simple is probably not what I'd say, but we can make it straightforward. >> Conceptually. >> Conceptually, yes. Conceptually it is the same. Because if you think about, firstly, we'll just take 5G for a moment, right? The 5G folks, if you look at the architecture for 5G, it was designed to run on a cloud architecture. It was designed to be a set of services that you could partition, and run in different places, whether it's in the region or at the edge. So in many ways it is sort of that simple. And let me give you an example. Two things, the first one is we announced integrated private wireless on AWS, which allows enterprise customers to come to a portal and look at the industry solutions. They're not worried about their network, they're worried about solving a problem, right? And they can come to that portal, they can find a solution, they can find a service provider that will help them with that solution. And what they end up with is a fully validated offering that AWS telco SAS have actually put to its paces to make sure this is a real thing. And whether they get it from a telco, and, and quite frankly in that space, it's SIs such as Federated that actually help our customers deploy those in private environments. So that's an example. And then added to that, we had a second announcement, which was AWS telco network builder, which allows telcos to plan, deploy, and operate at scale telco network capabilities on the cloud, think about it this way- >> As a managed service? >> As a managed service. So think about it this way. And the same way that enterprise IT has been deploying, you know, infrastructure as code for years. Telco network builder allows the telco folks to deploy telco networks and their capabilities as code. So it's not simple, but it is pretty straightforward. We're making it more straightforward as we go. >> Jump in Dave, by the way. He can geek out if you want. >> Yeah, no, no, no, that's good, that's good, that's good. But actually, I'm going to ask an AWS question, but I'm going to ask Iyad the AWS question. So when we, when I hear the word cloud from Wayne, cloud, AWS, typically in people's minds that denotes off-premises. Out there, AWS data center. In the telecom space, yes, of course, in the private 5G space, we're talking about a little bit of a different dynamic than in the public 5G space, in terms of the physical infrastructure. But regardless at the edge, there are things that need to be physically at the edge. Do you feel that AWS is sufficiently, have they removed the H word, hybrid, from the list of bad words you're not allowed to say? 'Cause there was a point in time- >> Yeah, of course. >> Where AWS felt that their growth- >> They'll even say multicloud today, (indistinct). >> No, no, no, no, no. But there was a period of time where, rightfully so, AWS felt that the growth trajectory would be supported solely by net new things off premises. Now though, in this space, it seems like that hybrid model is critical. Do you see AWS being open to the hybrid nature of things? >> Yeah, they're, absolutely. I mean, just to explain from- we're a services company and a solutions company. So we put together solutions at the edge, a smart campus, smart agriculture, a deployment. One of our biggest deployment is a million square feet warehouse automation project with the Marine Corps. >> That's bigger than the Fira. >> Oh yeah, it's bigger, definitely bigger than, you know, a small section of here. It's actually three massive warehouses. So yes, that is the edge. What the cloud is about is that massive amount of efficiency has happened by concentrating applications in data centers. And that is programmability, that is APIs that is solutions, that is applications that can run on it, where people know how to do it. And so all that efficiency now is being ported in a box called the edge. What AWS is doing for us is bringing all the business and technical solutions they had into the edge. Some of the data may send back and forth, but that's actually a smaller piece of the value for us. By being able to bring an AWS package at the edge, we're bringing IoT applications, we're bringing high speed cameras, we're able to integrate with the 5G public network. We're able to bring in identity and devices, we're able to bring in solutions for students, embedded laptops. All of these things that you can do much much faster and cheaper if you are able to tap in the 4,000, 5,000 partners and all the applications and all the development and all the models that AWS team did. By being able to bring that efficiency to the edge why reinvent that? And then along with that, there are partners that you, that help do integration. There are development done to make it hardened, to make the data more secure, more isolated. All of these things will contribute to an edge that truly is a carbon copy of the data center. >> So Wayne, it's AWS, Regardless of where the compute, networking and storage physically live, it's AWS. Do you think that the term cloud will sort of drift away from usage? Because if, look, it's all IT, in this case it's AWS and federated IT working together. How, what's your, it's sort of a obscure question about cloud, because cloud is so integrated. >> You Got this thing about cloud, it's just IT. >> I got thing about cloud too, because- >> You and Larry Ellison. >> Because it's no, no, no, I'm, yeah, well actually there's- >> There's a lot of IT that's not cloud, just say that okay. >> Now, a lot of IT that isn't cloud, but I would say- >> But I'll (indistinct) cloud is an IT tool, and you see AWS obviously with the Snow fill in the blank line of products and outpost type stuff. Fair to say that you're, doesn't matter where it is, it could be AWS if it's on the edge, right? >> Well, you know, everybody wants to define the cloud as what it may have been when it started. But if you look at what it was when it started and what it is today, it is different. But the ability to bring the experience, the AWS experience, the services, the operational experience and all the things that Iyad had been talking about from the region all to all the way to, you know, the IoT device, if you would, that entire continuum. And it doesn't matter where you start. Like if you start in region and you need to bring your value to other places because your customers are asking you to do so, we're enabling that experience where you need to bring it. If you started at the edge, and- but you want to build cloud value, you know, whether it's again, cost efficiency, scalability, AI, ML or analytics into those capabilities, you can start at the edge with the same APIs, with the same service, the same capabilities, and you can build that value in right from the get go. You don't build this bifurcation or many separations and try to figure out how do I glue them together? There is no gluing together. So if you think of cloud as being elastic, scalable flexible, where you can drive innovation, it's the same exact model on the continuum. And you can start at either end, it's up to you as a customer. >> And I think if, the key to me is the ecosystem. I mean, if you can do for this industry what you've done for the technology- enterprise technology business from an ecosystem standpoint, you know everybody talks about flywheel, but that gives you like the massive flywheel. I don't know what the ratio is, but it used to be for every dollar spent on a VMware license, $15 is spent in the ecosystem. I've never heard similar ratios in the AWS ecosystem, but it's, I go to reinvent and I'm like, there's some dollars being- >> That's a massive ecosystem. >> (indistinct). >> And then, and another thing I'll add is Jose Maria Alvarez, who's the chairman of Telefonica, said there's three pillars of the future-ready telco, low latency, programmable networks, and he said cloud and edge. So they recognizing cloud and edge, you know, low latency means you got to put the compute and the data, the programmable infrastructure was invented by Amazon. So what's the strategy around the telco edge? >> So, you know, at the end, so those are all great points. And in fact, the programmability of the network was a big theme in the show. It was a huge theme. And if you think about the cloud, what is the cloud? It's a set of APIs against a set of resources that you use in whatever way is appropriate for what you're trying to accomplish. The network, the telco network becomes a resource. And it could be described as a resource. We, I talked about, you know, network as in code, right? It's same infrastructure in code, it's telco infrastructure as code. And that code, that infrastructure, is programmable. So this is really, really important. And in how you build the ecosystem around that is no different than how we built the ecosystem around traditional IT abstractions. In fact, we feel that really the ecosystem is the killer app for 5G. You know, the killer app for 4G, data of sorts, right? We started using data beyond simple SMS messages. So what's the killer app for 5G? It's building this ecosystem, which includes the CSPs, the ISVs, all of the partners that we bring to the table that can drive greater value. It's not just about cost efficiency. You know, you can't save your way to success, right? At some point you need to generate greater value for your customers, which gives you better business outcomes, 'cause you can monetize them, right? The ecosystem is going to allow everybody to monetize 5G. >> 5G is like the dot connector of all that. And then developers come in on top and create new capabilities >> And how different is that than, you know, the original smartphones? >> Yeah, you're right. So what do you guys think of ChatGPT? (indistinct) to Amazon? Amazon turned the data center into an API. It's like we're visioning this world, and I want to ask that technologist, like, where it's turning resources into human language interfaces. You know, when you see that, you play with ChatGPT at all, or I know you guys got your own. >> So I won't speak directly to ChatGPT. >> No, don't speak from- >> But if you think about- >> Generative AI. >> Yeah generative AI is important. And, and we are, and we have been for years, in this space. Now you've been talking to AWS for a long time, and we often don't talk about things we don't have yet. We don't talk about things that we haven't brought to market yet. And so, you know, you'll often hear us talk about something, you know, a year from now where others may have been talking about it three years earlier, right? We will be talking about this space when we feel it's appropriate for our customers and our partners. >> You have talked about it a little bit, Adam Selipsky went on an interview with myself and John Furrier in October said you watch, you know, large language models are going to be enormous and I know you guys have some stuff that you're working on there. >> It's, I'll say it's exciting. >> Yeah, I mean- >> Well proof point is, Siri is an idiot compared to Alexa. (group laughs) So I trust one entity to come up with something smart. >> I have conversations with Alexa and Siri, and I won't judge either one. >> You don't need, you could be objective on that one. I definitely have a preference. >> Are the problems you guys solving in this space, you know, what's unique about 'em? What are they, can we, sort of, take some examples here (indistinct). >> Sure, the main theme is that the enterprise is taking control. They want to have their own networks. They want to focus on specific applications, and they want to build them with a skeleton crew. The one IT person in a warehouse want to be able to do it all. So what's unique about them is that they're now are a lot of automation on robotics, especially in warehousing environment agriculture. There simply aren't enough people in these industries, and that required precision. And so you need all that integration to make it work. People also want to build these networks as they want to control it. They want to figure out how do we actually pick this team and migrate it. Maybe just do the front of the house first. Maybe it's a security team that monitor the building, maybe later on upgrade things that use to open doors and close doors and collect maintenance data. So that ability to pick what you want to do from a new processors is really important. And then you're also seeing a lot of public-private network interconnection. That's probably the undercurrent of this show that haven't been talked about. When people say private networks, they're also talking about something called neutral host, which means I'm going to build my own network, but I want it to work, my Verizon (indistinct) need to work. There's been so much progress, it's not done yet. So much progress about this bring my own network concept, and then make sure that I'm now interoperating with the public network, but it's my domain. I can create air gaps, I can create whatever security and policy around it. That is probably the power of 5G. Now take all of these tiny networks, big networks, put them all in one ecosystem. Call it the Amazon marketplace, call it the Amazon ecosystem, that's 5G. It's going to be tremendous future. >> What does the future look like? We're going to, we just determined we're going to be orchestrating the network through human language, okay? (group laughs) But seriously, what's your vision for the future here? You know, both connectivity and cloud are on on a continuum. It's, they've been on a continuum forever. They're going to continue to be on a continuum. That being said, those continuums are coming together, right? They're coming together to bring greater value to a greater set of customers, and frankly all of us. So, you know, the future is now like, you know, this conference is the future, and if you look at what's going on, it's about the acceleration of the future, right? What we announced this week is really the acceleration of listening to customers for the last handful of years. And, we're going to continue to do that. We're going to continue to bring greater value in the form of solutions. And that's what I want to pick up on from the prior question. It's not about the network, it's not about the cloud, it's about the solutions that we can provide the customers where they are, right? And if they're on their mobile phone or they're in their factory floor, you know, they're looking to accelerate their business. They're looking to accelerate their value. They're looking to create greater safety for their employees. That's what we can do with these technologies. So in fact, when we came out with, you know, our announcement for integrated private wireless, right? It really was about industry solutions. It really isn't about, you know, the cloud or the network. It's about how you can leverage those technologies, that continuum, to deliver you value. >> You know, it's interesting you say that, 'cause again, when we were interviewing Adam Selipsky, everybody, you know, all journalists analysts want to know, how's Adam Selipsky going to be different from Andy Jassy, what's the, what's he going to do to Amazon to change? And he said, listen, the real answer is Amazon has changed. If Andy Jassy were here, we'd be doing all, you know, pretty much the same things. Your point about 17 years ago, the cloud was S3, right, and EC2. Now it's got to evolve to be solutions. 'Cause if that's all you're selling, is the bespoke services, then you know, the future is not as bright as the past has been. And so I think it's key to look for what are those outcomes or solutions that customers require and how you're going to meet 'em. And there's a lot of challenges. >> You continue to build value on the value that you've brought, and you don't lose sight of why that value is important. You carry that value proposition up the stack, but the- what you're delivering, as you said, becomes maybe a bigger or or different. >> And you are getting more solution oriented. I mean, you're not hardcore solutions yet, but we're seeing more and more of that. And that seems to be a trend. We've even seen in the database world, making things easier, connecting things. Not really an abstraction layer, which is sort of antithetical to your philosophy, but it creates a similar outcome in terms of simplicity. Yeah, you're smiling 'cause you guys always have a different angle, you know? >> Yeah, we've had this conversation. >> It's right, it's, Jassy used to say it's okay to be misunderstood. >> That's Right. For a long time. >> Yeah, right, guys, thanks so much for coming to theCUBE. I'm so glad we could make this happen. >> It's always good. Thank you. >> Thank you so much. >> All right, Dave Nicholson, for Lisa Martin, Dave Vellante, John Furrier in the Palo Alto studio. We're here at the Fira, wrapping out MWC23. Keep it right there, thanks for watching. (upbeat music)